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create mode 100644 _freeze/site_libs/ionicons-2.0.1/ionicons.min.css create mode 100644 _freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.css create mode 100644 _freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.js create mode 100644 _freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.min.js create mode 100644 _freeze/site_libs/leaflet-easybutton-1.3.1/EasyButton-binding.js create mode 100644 _freeze/site_libs/leaflet-easybutton-1.3.1/LICENSE create mode 100644 _freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.css create mode 100644 _freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.js diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json index c3caaa9..bef1479 100644 --- a/_freeze/index/execute-results/html.json +++ b/_freeze/index/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "32601e0046b1993cb7e1068957566c37", + "hash": "49b25671532cb9e52c94203abbae0ad7", "result": { - "markdown": "---\ntitle: \n \"![](www/images/act-logo.png){width=6in}\n
\n Rochester Region Federal Funds Dashboard\"\nsubtitle: \"How the Rochester Region Is Spending Federal Recovery Dollars\"\ndate: '2023/08/22'\ndate-format: \"MMMM D, YYYY\"\nlanguage:\n title-block-published: \"Data as of\"\neditor_options: \n chunk_output_type: console\nexecute:\n echo: false\n message: false\n warning: false\n error: false\ncss: style.css\nknitr:\n opts_chunk: \n dev: \"ragg_png\"\n---\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\nIn response to the economic impacts of the COVID-19 pandemic, the federal government provided unprecedented levels of flexible relief funds to local governments across the country, [including Rochester](https://www.urban.org/sites/default/files/publication/105119/harnessing-federal-funds-for-inclusive-recovery-in-rochester-ny_0.pdf)\n. These federal recovery funds [offer an unparalleled opportunity](https://www.urban.org/research/publication/how-equity-helps-lift-all-boats) to not only stimulate economic recovery from the pandemic, but to also increase equity. To do so, the funds must be used to ensure that everyone, especially historically excluded groups, can benefit from and contribute to economic recovery. [Research](https://www.urban.org/research/publication/inclusive-recovery-us-cities) has demonstrated that cities can recover from economic distress while improving on inclusion and equity. However, this is not a given. Many cities lose ground on either racial or economic inclusion during a recovery by allowing funds to flow to business as usual. \n \nTo increase transparency in this process, this dashboard shares data on how the City of Rochester and Monroe County allocated and spent a portion of the flexible recovery funds. The dashboard focuses on [State and Local Fiscal Recovery Funds (SLFRF)](https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments/state-and-local-fiscal-recovery-funds#:~:text=The%20Coronavirus%20State%20and%20Local,COVID%2D19%20public%20health%20emergency) provided through the American Rescue Plan Act (ARPA) because they were one of the most flexible funding sources, giving local governments authority to decide how they should be spent. And, they tended to be spent more on longer term recovery efforts than on short term emergency public health response, which was the focus of CARES Act (Coronavirus Aid, Relief, and Economic Security Act) dollars. SLFRF must be obligated by the end of 2024 and spent by the end of 2026. \n \nThe dashboard starts by showing what percent of the allocations align with the building blocks of an inclusive recovery, which are five strategies for spending that direct funds to support equity and inclusion. As of 2023/08/22 date, 61% of the dollars in the City of Rochester and Monroe County were allocated toward programs and initiatives that align with these building blocks. The dashboard then shows more broadly how funds are being spent by policy area and subtopic. Next, it explores how well the allocations align with Rochester Area Community Foundation’s priorities. Finally, it shows how the City of Rochester’s SLFRF capital investments map onto racial characteristics of neighborhoods within the region, as well as how they overlap with the original redlining maps for the city. (Redlining refers to the process in which borrowers are denied access to credit due to the racial composition of their neighborhood.) Finally, at the end of the dashboard is a [table](#programs-table). showing all of the allocations and their descriptions in detail. \n \nNote that this dashboard does not track outcomes from these investments – it only shows where the dollars are flowing. Simply allocating funds toward topics that could increase equity and inclusion does not guarantee that they do so. Future research efforts should closely monitor outcomes and impacts from the recovery dollar investments to ensure that they close equity gaps exacerbated by the pandemic and address the root causes of inequities. \n \n\n## How Much is Being Spent on an Inclusive Recovery?\n\n[Previous work by the Urban Institute](https://www.urban.org/sites/default/files/publication/105290/aligning-the-use-of-recovery-funds-with-community-goals-in-rochester-new-york.pdf) identified five building blocks of an inclusive recovery. These building blocks were created in collaboration with community stakeholders across the country and include: \n\n1. **Create jobs** for residents hardest hit by the pandemic or who face the\n greatest barriers to employment\n2. **Connect residents to jobs** and economic opportunities, including through\n workforce development, child care, transportation, or broadband\n3. **Reinvest in disinvested communities** and address long-standing disparities in\n access to education, capital, economic opportunities, and climate resilience\n4. **Stabilize housing and expand affordable housing** options for low-income\n households and housing-insecure renters\n5. Create opportunities for low-wealth households to **build wealth**.\n\n\n::: {.cell}\n\n:::\n\nAcross all allocations in the Rochester region, **55%** of the funding has been allocated towards these strategies that promote inclusive recovery and equitable growth. The figure below shows the total amount of funding for Rochester and Monroe County that was allocated to programs that align with these building blocks. \n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n:::\n\n\n## What types of inclusive recovery strategies are supported?\n\n \nOut of the funds allocated to programs that align with the five building blocks of an inclusive recovery, the most funding has been allocated to reinvesting in disinvested communities, while the least has been allocated to creating jobs for residents hardest hit by the pandemic or who face the greatest barriers to employment. The figure below shows how that funding is divided among the five building blocks and includes only the funds allocated to programs that align with the building blocks. \n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n\n\n\n## What policy areas are being funded?\n\nZooming out, overall, $286M of SLFRF funds have been allocated to the City of Rochester and Monroe County, and 8% have been reported as spent. The figure below shows how that funding has been allocated across policy categories and includes all allocations, not just those that align with the building blocks of an inclusive recovery. To view individual project allocations and their policy categories, see the funding table at the end of the dashboard. \n \nBy category, most of the funds have been allocated to Community and Economic Development, Infrastructure, and Housing, while the least have been allocated to Operations, Public Safety, and Social Services. \n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\n::: panel-tabset\n\n## Allocated\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\n## Spent\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-9-1.png){width=672}\n:::\n:::\n\n\n:::\n\n## What subtopics are funded within each policy area?\n\nThe figure below provides a more detailed breakdown of allocations to each policy area. Each policy area tab breaks down the allocations into subtopics. For example, most of the money allocated to community and economic development has gone to the workforce development subtopic. To view individual project allocations and their subtopic categories, see the funding table at the end of the dashboard. \n\n::: panel-tabset\n\n### Community and Economic Development\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n\n### Housing\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-11-1.png){width=672}\n:::\n:::\n\n\n\n### Infrastructure\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-12-1.png){width=672}\n:::\n:::\n\n\n\n### Operations\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-13-1.png){width=672}\n:::\n:::\n\n\n\n### Public Health\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n\n\n\n\n### Public Safety\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-15-1.png){width=672}\n:::\n:::\n\n\n### Social Services\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-16-1.png){width=672}\n:::\n:::\n\n\n:::\n\n## How does funding align with the Rochester Area Community Foundation’s priorities? \n\nThe Rochester Area Community Foundation’s key priorities for investment in the region include closing the academic achievement and opportunity gap, fostering racial and ethnic understanding and equity, partnering against poverty, supporting arts and culture, preserving historic assets, advancing environmental justice and sustainability, and promoting successful aging. Overall, 43% of SLFRF funding has been allocated towards these priority areas. The figure below shows how SLFRF funding is divided among the priority topics and includes only the funds allocated to programs that align with one of RACF’s priority areas. \n\n\n::: {.cell}\n\n:::\n\n\n\nThe Rochester Area Community Foundation's key priorities for investment in the\nregion include closing the academic achievement and opportunity gap, fostering\nracial and ethnic understanding and equity, partnering against poverty,\nsupporting arts and culture, preserving historic assets, advancing environmental\njustice and sustainability, and promoting successful aging. Overall, **46%** of the\nFederal funding has been allocated towards these priority areas:\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n\n\n## Which neighborhoods are the funds being spent in? \n\nThe map below shows how the City of Rochester’s SLFRF capital investments map onto racial characteristics of neighborhoods, as well as the original Federal redlining maps for the city. Click on the tabs at the top to see how they overlap with the percent of residents who are Black in a neighborhood and the percent of residents who are Latine in a neighborhood. \n \nRedlining refers to the system that the Federal Housing Administration and the Home Owners’ Loan Corporation used to grade the profitability of neighborhoods in the late 1930s. The four categories were green (“best”), blue (“still desirable”), yellow (“definitely declining”), and red (“hazardous”). These grades were largely based on the neighborhood’s racial, ethnic, socioeconomic, and religious composition. Generally, White, middle-class neighborhoods received FHA home loans, whereas many Black and Latine neighborhoods were deemed hazardous and declining in value and did not receive FHA insured mortgages or loans. These maps [had long lasting effects](https://www.econstor.eu/bitstream/10419/200568/1/1010730592.pdf) on racial segregation, homeownership, and house values in redlined neighborhoods. \n \nTo increase equity in the region, leaders must overinvest in communities that have been underinvested in historically. These investments must be those that [benefit incumbent residents](https://www.urban.org/research/publication/what-policies-and-programs-can-support-inclusive-recovery-rochester-new-york), meaning that they should not be investments that only benefit new, incoming wealthier residents, or those that have negative impacts on immediate neighbors, like a sewage treatment facility or a highway that pollutes the air. And, they must be investments that [community members want](https://www.urban.org/research/publication/aligning-use-recovery-funds-community-goals-rochester-new-york), particularly historically excluded members. The maps below give us a first approximation of how equitably investments are distributed, but further exploration of how these investments align with the goals of community members and how much they benefit historically excluded residents is necessary to ensure that they are increasing equity within those communities. \n\n::: panel-tabset\n\n### Redlining\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\n\n### Percent Black\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n\n### Percent Latine\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n\n\n**Note:** This dashboard uses the term “Latine” because some consider it a more inclusive term than “Hispanic,” because it includes people from all countries in Latin America, and than “Latino/a” because it is a gender neutral term. Percentages shown in this map were based on American Community Survey data for “Hispanic or Latino” population.\n:::\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n:::\n\n
\n\n\n## Explore All Programs Funded {#programs-table}\nThis table shows all programs that the City of Rochester and Monroe County have allocated SLFRF funds to, as well as their building block, policy area, policy subtopic, and RACF priority area category. The default display on the table shows the programs in order from largest to smallest allocation. You can use the arrows next to each column title to sort the table by that column, and you can use the search bars under each column or the overall search bar to search the table. \n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n
\n\n
\n```\n:::\n:::\n\n\n
\n\n## About the Dashboard\n\nThis dashboard was created by the [Urban Institute](https://www.urban.org/) in partnership with and support from [The Rochester Area Community Foundation (RACF)](https://www.racf.org/) to visualize Monroe County and the City of Rochester’s ARPA spending by the five building blocks of inclusive recovery, policy category, and RACF’s investment priorities. By tracking recovery funding expenditures, this dashboard allows us to monitor public spending by the categories most critical in supporting an inclusive recovery from the COVID-19 pandemic. \n \n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n\n\n\n```\n:::\n:::\n\n\n
\n\nFor more information about the dashboard, please contact [Meg Norris](mailto:mnorris@racf.org) (The Rochester Area community Foundation) or [Christina Stacy](mailto:cstacy@urban.org) (Urban Institute).\n\nClick For [Glossary of Terms](glossary.qmd)\n\nThe code used to create this dashboard was writen by [Manuel Alcalá Kovalski](mailto:malcalakovalski@urban.org) and can be found on\n[GitHub](https://github.com/UI-Research/rochester-dashboard).\n\n", + "markdown": "---\ntitle: \n \"![](www/images/act-logo.png){width=6in}\n
\n Rochester Region Federal Funds Dashboard\"\nsubtitle: \"How the Rochester Region Is Spending Federal Recovery Dollars\"\ndate: '08/22/23'\ndate-format: \"MMMM D, YYYY\"\nlanguage:\n title-block-published: \"Data as of\"\neditor_options: \n chunk_output_type: console\nexecute:\n echo: false\n message: false\n warning: false\n error: false\ncss: style.css\nknitr:\n opts_chunk: \n dev: \"ragg_png\"\n---\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\nThe federal government provided unprecedented levels of flexible relief funds to\nlocal governments across the country, [including those in the Rochester\nregion](https://www.urban.org/sites/default/files/publication/105119/harnessing-federal-funds-for-inclusive-recovery-in-rochester-ny_0.pdf),\nto help them recover from the economic impacts of the COVID-19 pandemic. As the\nCity of Rochester and Monroe County continue to allocate and spend these federal\nrecovery funds, they have an unparalleled opportunity to not only stimulate\neconomic recovery from the pandemic, but to also increase equity and remediate\nsome of the past inequities that have been [built into our nation's\nlaws](https://books.google.com/books?hl=en&lr=&id=SdtDDQAAQBAJ&oi=fnd&pg=PT4&dq=color+of+law&ots=RL_u4QqRBH&sig=eYYnHvwibwB_XBfpzr0jQVK2SOs#v=onepage&q=color%20of%20law&f=false)\nand programs. To do so, the funds [must be\nused](https://www.urban.org/research/publication/inclusive-recovery-us-cities)\nto ensure that everyone--especially historically excluded groups--can benefit\nfrom and contribute to economic growth.\n\nTo increase transparency in this process, this dashboard tracks data on how the\nCity of Rochester and Monroe County have allocated and spent a portion of these\nrecovery funds: [State and Local Fiscal Recovery\nFunds](https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments/state-and-local-fiscal-recovery-funds#:~:text=The%20Coronavirus%20State%20and%20Local,COVID%2D19%20public%20health%20emergency)\n(SLFRF) provided through the American Rescue Plan Act (ARPA). SLFRF must be\nobligated by the end of 2024 and spent by the end of 2026. The dashboard focuses\non SLFRF because of their flexibility in giving local governments authority to\ndecide how they should be spent. And, they tended to be on longer term recovery\nefforts rather than on short term emergency public health response, which was\nthe focus of Coronavirus Aid, Relief, and Economic Security Act (CARES Act)\ndollars.\n\nThe dashboard starts by showing what percent of the allocations align with the\nbuilding blocks of an inclusive recovery, which are five strategies for spending\nthat direct funds to support equity and inclusion. These five building blocks\nare: create jobs for those who face the greatest barriers to employment, connect\nhistorically excluded residents to jobs and economic opportunities, reinvest in\ndisinvested communities, stabilize housing and expand affordable housing, and\ncreate opportunities for low-wealth households to build wealth. As of\n08/22/23, 65% of the dollars in the City of Rochester and Monroe\nCounty were allocated toward programs and initiatives that align with these\nbuilding blocks.\n\nThe dashboard then shows more broadly how funds are being spent by policy area\nand subtopic. Next, it explores how well the allocations align with Rochester\nArea Community Foundation's priorities. Finally, it shows how the City of\nRochester's SLFRF capital investments map onto racial characteristics of\nneighborhoods within the region, as well as how they overlap with the original\nredlining maps for the city. (Redlining refers to the process in which borrowers\nare denied access to credit due to the racial composition of their\nneighborhood.) At the end of the dashboard is a table showing all of the\nallocations and their descriptions in detail.\n\nNote that this dashboard does not track outcomes from these investments -- it\nonly shows where the dollars are flowing. Simply allocating funds toward topics\nthat could increase equity and inclusion does not guarantee that they do so.\nFuture research efforts should closely monitor outcomes and impacts from the\nrecovery dollar investments to ensure that they close equity gaps exacerbated by\nthe pandemic and address the root causes of inequities.\n\n## How Much is Being Spent on an Inclusive Recovery?\n\n[Previous work by the Urban\nInstitute](https://www.urban.org/sites/default/files/publication/105290/aligning-the-use-of-recovery-funds-with-community-goals-in-rochester-new-york.pdf)\nidentified five building blocks of an inclusive recovery. These building blocks\nwere created in collaboration with community stakeholders across the country and\ninclude:\n\n1. **Create jobs** for residents hardest hit by the pandemic or who face the\n greatest barriers to employment\n2. **Connect residents to jobs** and economic opportunities, including through\n workforce development, child care, transportation, or broadband\n3. **Reinvest in disinvested communities** and address long-standing\n disparities in access to education, capital, economic opportunities, and\n climate resilience\n4. **Stabilize housing and expand affordable housing** options for low-income\n households and housing-insecure renters\n5. Create opportunities for low-wealth households to **build wealth**.\n\nAcross all allocations for the city and county, **65%** of the funding has\nbeen allocated towards these strategies that promote inclusive recovery and\nequitable growth. The figure below shows the total amount of funding for\nRochester and Monroe County that was allocated to programs that align with these\nbuilding blocks, based on review of the program description and goals.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-4-1.png){width=672}\n:::\n:::\n\n\n## What types of inclusive recovery strategies are supported?\n\nOut of the funds allocated to programs that align with the five building blocks\nof an inclusive recovery, the most funding has been allocated to reinvesting in\ndisinvested communities, while the least has been allocated to creating jobs for\nresidents hardest hit by the pandemic or who face the greatest barriers to\nemployment. The figure below shows how that funding is divided among the five\nbuilding blocks and includes only the funds allocated to programs that align\nwith the building blocks.\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-5-1.png){width=672}\n:::\n:::\n\n\n## What policy areas are being funded?\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n \n \nThe City of Rochester and Monroe County have allocated almost **$301M** of SLFRF funds, and, of those, **14.6%** have been reported as spent. The figure below shows how that funding has been allocated across policy categories and includes all allocations, not just those that align with the building blocks of an inclusive recovery. \n \nBy category, most of the funds have been allocated to Community and Economic Development, Infrastructure, and Housing, while the least have been allocated to Operations, Public Health and COVID-19 Response, and Social Services. \n:::\n\n\n::: panel-tabset\n## Allocated\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-8-1.png){width=672}\n:::\n:::\n\n\n## Spent\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-9-1.png){width=672}\n:::\n:::\n\n:::\n\n## What subtopics are funded within each policy area?\n\nThe figure below provides a more detailed breakdown of allocations to each\npolicy area. Each policy area tab breaks down the allocations into subtopics.\nFor example, most of the money allocated to community and economic development\nhas gone to workforce development.\n\n::: panel-tabset\n### Community and Economic Development\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-10-1.png){width=672}\n:::\n:::\n\n\n### Infrastructure\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-11-1.png){width=672}\n:::\n:::\n\n\n### Housing\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-12-1.png){width=672}\n:::\n:::\n\n\n### Public Safety\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-13-1.png){width=672}\n:::\n:::\n\n\n### Social Services\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-14-1.png){width=672}\n:::\n:::\n\n\n### Public Health\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-15-1.png){width=672}\n:::\n:::\n\n\n### Operations\n\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-16-1.png){width=672}\n:::\n:::\n\n:::\n\n## How does funding align with the Rochester Area Community Foundation's priorities?\n\n\n::: {.cell}\n\n:::\n\n\nThe Rochester Area Community Foundation's key priorities for investment in the\nregion include:\n\n- Closing the academic achievement and opportunity gap;\n\n- Fostering racial and ethnic understanding and equity;\n\n- Partnering against poverty;\n\n- Supporting arts and culture; Preserving historic assets;\n\n- Advancing environmental justice and sustainability; and\n\n- Promoting successful aging.\n\n\n::: {.cell}\n\nOverall, **38.9%** of SLFRF funding has been allocated towards these priority areas. The figure below shows how SLFRF funding is divided among the priority topics and includes only the funds allocated to programs that align with one of RACF's priority areas.\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![Source: Urban Institute calculations of data from City of Rochester ARPA Reporting Dashboard and Monroe County Recovery Plan 2023 Annual Report](index_files/figure-html/unnamed-chunk-18-1.png){width=672}\n:::\n:::\n\n\n## Which neighborhoods are the funds being spent in?\n\nThe map below shows how the City of Rochester's SLFRF capital investments map\nonto racial characteristics of neighborhoods, as well as the original Federal\nredlining maps for the city. Click on the tabs at the top to see how they\noverlap with the percent of residents who are Black in a neighborhood and the\npercent of residents who are Hispanic/Latino[^1].\n\n[^1]: We use the term \"Hispanic/Latino\" in this map because this is the term\n used in the data collected by the American Community Survey. We recognize\n that this may not be the preferred identifier for all, and we remain\n committed to employing inclusive language whenever possible. \n\n[Redlining](https://www.npr.org/2017/05/03/526655831/a-forgotten-history-of-how-the-u-s-government-segregated-america)\nrefers to the system that the Federal Housing Administration and the Home\nOwners' Loan Corporation used to grade the profitability of neighborhoods in the\nlate 1930s. The four categories were green (\"best\"), blue (\"still desirable\"),\nyellow (\"definitely declining\"), and red (\"hazardous\"). These grades were\nlargely based on the neighborhood's racial, ethnic, socioeconomic, and religious\ncomposition. Generally, White, middle-class neighborhoods received FHA home\nloans, whereas many Black and Hispanic/Latino neighborhoods were deemed\nhazardous and declining in value and did not receive FHA insured mortgages or\nloans. These maps [had long lasting\neffects](https://www.econstor.eu/bitstream/10419/200568/1/1010730592.pdf) on\nracial segregation, homeownership, and house values in redlined neighborhoods.\n\nTo increase equity in the region, leaders must overinvest in communities that\nhave been underinvested in historically. These investments must be those that\n[benefit incumbent\nresidents](https://www.urban.org/research/publication/what-policies-and-programs-can-support-inclusive-recovery-rochester-new-york),\nmeaning that they should not be investments that only benefit new, incoming\nwealthier residents, or those that have negative impacts on immediate neighbors,\nlike a sewage treatment facility or a highway that pollutes the air. And, they\nmust be investments that [community members\nwant](https://www.urban.org/research/publication/aligning-use-recovery-funds-community-goals-rochester-new-york),\nparticularly historically excluded members. The maps below give us a first\napproximation of how equitably investments are distributed, but further\nexploration of how these investments align with the goals of community members\nand how much they benefit historically excluded residents is necessary to ensure\nthat they are increasing equity within those communities. \n\n::: panel-tabset\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n\n### Redlining\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\n### Percent Black\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n\n### Percent Hispanic/Latino\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n```\n:::\n:::\n\n:::\n\n
\n\n## Explore All Programs Funded {#programs-table}\n\nThis table shows all programs that the City of Rochester and Monroe County have\nallocated SLFRF funds to, as well as their building block, policy area, policy\nsubtopic, and RACF priority area category. The default display on the table\nshows the programs in order from largest to smallest allocation. You can use the\narrows next to each column title to sort the table by that column, and you can\nuse the search bars under each column or the overall search bar to search the\ntable. \n\nNote: Data on spending were only available from the City of Rochester \n\nData current as of: 08/22/23\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n
\n\n
\n\n
\n```\n:::\n:::\n\n\n
\n\n## About the Dashboard\n\nThis dashboard was created by the [Urban Institute](https://www.urban.org/) in\npartnership with and support from [The Rochester Area Community Foundation\n(RACF)](https://www.racf.org/) to visualize Monroe County and the City of\nRochester's ARPA spending by the five building blocks of inclusive recovery,\npolicy category, and RACF's investment priorities. By tracking recovery funding\nexpenditures, this dashboard allows us to monitor public spending by the\ncategories most critical in supporting an inclusive recovery from the COVID-19\npandemic.\n\n\n::: {.cell}\n::: {.cell-output-display}\n```{=html}\n\n\n\n```\n:::\n:::\n\n\n
\n\nFor more information about the dashboard, please contact [Meg\nNorris](mailto:mnorris@racf.org) (The Rochester Area community Foundation) or\n[Christina Stacy](mailto:cstacy@urban.org) (Urban Institute).\n\nClick For [Glossary of Terms](glossary.qmd)\n\nThe code used to create this dashboard was writen by [Manuel Alcalá\nKovalski](mailto:malcalakovalski@urban.org) and can be found on\n[GitHub](https://github.com/UI-Research/rochester-dashboard).\n", "supporting": [ "index_files" ], @@ -10,7 +10,7 @@ ], "includes": { "include-in-header": [ - "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" + "\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" ] }, "engineDependencies": {}, diff --git a/_freeze/index/figure-html/unnamed-chunk-11-1.png b/_freeze/index/figure-html/unnamed-chunk-11-1.png index 640691058f7a5600589bbfd15452aff86bd7305c..e95395164e56f4967dacf30af2dfe34ab1d7c105 100644 GIT binary patch literal 29377 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nt:"\f083"}.fa-key:before{content:"\f084"}.fa-gears:before,.fa-cogs:before{content:"\f085"}.fa-comments:before{content:"\f086"}.fa-thumbs-o-up:before{content:"\f087"}.fa-thumbs-o-down:before{content:"\f088"}.fa-star-half:before{content:"\f089"}.fa-heart-o:before{content:"\f08a"}.fa-sign-out:before{content:"\f08b"}.fa-linkedin-square:before{content:"\f08c"}.fa-thumb-tack:before{content:"\f08d"}.fa-external-link:before{content:"\f08e"}.fa-sign-in:before{content:"\f090"}.fa-trophy:before{content:"\f091"}.fa-github-square:before{content:"\f092"}.fa-upload:before{content:"\f093"}.fa-lemon-o:before{content:"\f094"}.fa-phone:before{content:"\f095"}.fa-square-o:before{content:"\f096"}.fa-bookmark-o:before{content:"\f097"}.fa-phone-square:before{content:"\f098"}.fa-twitter:before{content:"\f099"}.fa-facebook-f:before,.fa-facebook:before{content:"\f09a"}.fa-github:before{content:"\f09b"}.fa-unlock:before{content:"\f09c"}.fa-credit-card:before{content:"\f09d"}.fa-feed:before,.fa-rss:before{content:"\f09e"}.fa-hdd-o:before{content:"\f0a0"}.fa-bullhorn:before{content:"\f0a1"}.fa-bell:before{content:"\f0f3"}.fa-certificate:before{content:"\f0a3"}.fa-hand-o-right:before{content:"\f0a4"}.fa-hand-o-left:before{content:"\f0a5"}.fa-hand-o-up:before{content:"\f0a6"}.fa-hand-o-down:before{content:"\f0a7"}.fa-arrow-circle-left:before{content:"\f0a8"}.fa-arrow-circle-right:before{content:"\f0a9"}.fa-arrow-circle-up:before{content:"\f0aa"}.fa-arrow-circle-down:before{content:"\f0ab"}.fa-globe:before{content:"\f0ac"}.fa-wrench:before{content:"\f0ad"}.fa-tasks:before{content:"\f0ae"}.fa-filter:before{content:"\f0b0"}.fa-briefcase:before{content:"\f0b1"}.fa-arrows-alt:before{content:"\f0b2"}.fa-group:before,.fa-users:before{content:"\f0c0"}.fa-chain:before,.fa-link:before{content:"\f0c1"}.fa-cloud:before{content:"\f0c2"}.fa-flask:before{content:"\f0c3"}.fa-cut:before,.fa-scissors:before{content:"\f0c4"}.fa-copy:before,.fa-files-o:before{content:"\f0c5"}.fa-paperclip:before{content:"\f0c6"}.fa-save:before,.fa-floppy-o:before{content:"\f0c7"}.fa-square:before{content:"\f0c8"}.fa-navicon:before,.fa-reorder:before,.fa-bars:before{content:"\f0c9"}.fa-list-ul:before{content:"\f0ca"}.fa-list-ol:before{content:"\f0cb"}.fa-strikethrough:before{content:"\f0cc"}.fa-underline:before{content:"\f0cd"}.fa-table:before{content:"\f0ce"}.fa-magic:before{content:"\f0d0"}.fa-truck:before{content:"\f0d1"}.fa-pinterest:before{content:"\f0d2"}.fa-pinterest-square:before{content:"\f0d3"}.fa-google-plus-square:before{content:"\f0d4"}.fa-google-plus:before{content:"\f0d5"}.fa-money:before{content:"\f0d6"}.fa-caret-down:before{content:"\f0d7"}.fa-caret-up:before{content:"\f0d8"}.fa-caret-left:before{content:"\f0d9"}.fa-caret-right:before{content:"\f0da"}.fa-columns:before{content:"\f0db"}.fa-unsorted:before,.fa-sort:before{content:"\f0dc"}.fa-sort-down:before,.fa-sort-desc:before{content:"\f0dd"}.fa-sort-up:before,.fa-sort-asc:before{content:"\f0de"}.fa-envelope:before{content:"\f0e0"}.fa-linkedin:before{content:"\f0e1"}.fa-rotate-left:before,.fa-undo:before{content:"\f0e2"}.fa-legal:before,.fa-gavel:before{content:"\f0e3"}.fa-dashboard:before,.fa-tachometer:before{content:"\f0e4"}.fa-comment-o:before{content:"\f0e5"}.fa-comments-o:before{content:"\f0e6"}.fa-flash:before,.fa-bolt:before{content:"\f0e7"}.fa-sitemap:before{content:"\f0e8"}.fa-umbrella:before{content:"\f0e9"}.fa-paste:before,.fa-clipboard:before{content:"\f0ea"}.fa-lightbulb-o:before{content:"\f0eb"}.fa-exchange:before{content:"\f0ec"}.fa-cloud-download:before{content:"\f0ed"}.fa-cloud-upload:before{content:"\f0ee"}.fa-user-md:before{content:"\f0f0"}.fa-stethoscope:before{content:"\f0f1"}.fa-suitcase:before{content:"\f0f2"}.fa-bell-o:before{content:"\f0a2"}.fa-coffee:before{content:"\f0f4"}.fa-cutlery:before{content:"\f0f5"}.fa-file-text-o:before{content:"\f0f6"}.fa-building-o:before{content:"\f0f7"}.fa-hospital-o:before{content:"\f0f8"}.fa-ambulance:before{content:"\f0f9"}.fa-medkit:before{content:"\f0fa"}.fa-fighter-jet:before{content:"\f0fb"}.fa-beer:before{content:"\f0fc"}.fa-h-square:before{content:"\f0fd"}.fa-plus-square:before{content:"\f0fe"}.fa-angle-double-left:before{content:"\f100"}.fa-angle-double-right:before{content:"\f101"}.fa-angle-double-up:before{content:"\f102"}.fa-angle-double-down:before{content:"\f103"}.fa-angle-left:before{content:"\f104"}.fa-angle-right:before{content:"\f105"}.fa-angle-up:before{content:"\f106"}.fa-angle-down:before{content:"\f107"}.fa-desktop:before{content:"\f108"}.fa-laptop:before{content:"\f109"}.fa-tablet:before{content:"\f10a"}.fa-mobile-phone:before,.fa-mobile:before{content:"\f10b"}.fa-circle-o:before{content:"\f10c"}.fa-quote-left:before{content:"\f10d"}.fa-quote-right:before{content:"\f10e"}.fa-spinner:before{content:"\f110"}.fa-circle:before{content:"\f111"}.fa-mail-reply:before,.fa-reply:before{content:"\f112"}.fa-github-alt:before{content:"\f113"}.fa-folder-o:before{content:"\f114"}.fa-folder-open-o:before{content:"\f115"}.fa-smile-o:before{c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diff --git a/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.css b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.css new file mode 100644 index 0000000..363e239 --- /dev/null +++ b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.css @@ -0,0 +1,128 @@ +/* +Author: L. Voogdt +License: MIT +Version: 1.0 +*/ + +/* Marker setup */ +.awesome-marker { + background: url('images/markers-soft.png') no-repeat 0 0; + width: 35px; + height: 46px; + position:absolute; + left:0; + top:0; + display: block; + text-align: center; +} + +.awesome-marker-shadow { + background: url('images/markers-shadow.png') no-repeat 0 0; + width: 36px; + height: 16px; +} + +/* Retina displays */ +@media (min--moz-device-pixel-ratio: 1.5),(-o-min-device-pixel-ratio: 3/2), +(-webkit-min-device-pixel-ratio: 1.5),(min-device-pixel-ratio: 1.5),(min-resolution: 1.5dppx) { + .awesome-marker { + background-image: url('images/markers-soft@2x.png'); + background-size: 720px 92px; + } + .awesome-marker-shadow { + background-image: url('images/markers-shadow@2x.png'); + background-size: 35px 16px; + } +} + +.awesome-marker i { + color: #333; + margin-top: 10px; + display: inline-block; + font-size: 14px; +} + +.awesome-marker .icon-white { + color: #fff; +} + +/* Colors */ +.awesome-marker-icon-red { + background-position: 0 0; +} + +.awesome-marker-icon-darkred { + background-position: -180px 0; +} + +.awesome-marker-icon-lightred { + background-position: -360px 0; +} + +.awesome-marker-icon-orange { + background-position: -36px 0; +} + +.awesome-marker-icon-beige { + background-position: -396px 0; +} + +.awesome-marker-icon-green { + background-position: -72px 0; +} + +.awesome-marker-icon-darkgreen { + background-position: -252px 0; +} + +.awesome-marker-icon-lightgreen { + background-position: -432px 0; +} + +.awesome-marker-icon-blue { + background-position: -108px 0; +} + +.awesome-marker-icon-darkblue { + background-position: -216px 0; +} + +.awesome-marker-icon-lightblue { + background-position: -468px 0; +} + +.awesome-marker-icon-purple { + background-position: -144px 0; +} + +.awesome-marker-icon-darkpurple { + background-position: -288px 0; +} + +.awesome-marker-icon-pink { + background-position: -504px 0; +} + +.awesome-marker-icon-cadetblue { + background-position: -324px 0; +} + +.awesome-marker-icon-white { + background-position: -576px 0; +} + +.awesome-marker-icon-gray { + background-position: -648px 0; +} + +.awesome-marker-icon-lightgray { + background-position: -612px 0; +} + +.awesome-marker-icon-black { + background-position: -682px 0; +} + +.awesome-marker-square { + background-position-y: -46px; +} diff --git a/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.js b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.js new file mode 100644 index 0000000..0b5f483 --- /dev/null +++ b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.js @@ -0,0 +1,146 @@ +/* + Leaflet.AwesomeMarkers, a plugin that adds colorful iconic markers for Leaflet, based on the Font Awesome icons + (c) 2012-2013, Lennard Voogdt + + https://leafletjs.com + https://github.com/lvoogdt + */ + +/*global L*/ + +(function (window, document, undefined) { + "use strict"; + /* + * Leaflet.AwesomeMarkers assumes that you have already included the Leaflet library. + */ + + L.AwesomeMarkers = {}; + + L.AwesomeMarkers.version = '2.0.1'; + + L.AwesomeMarkers.Icon = L.Icon.extend({ + options: { + iconSize: [35, 45], + iconAnchor: [17, 42], + popupAnchor: [1, -32], + shadowAnchor: [10, 12], + shadowSize: [36, 16], + className: 'awesome-marker', + prefix: 'glyphicon', + spinClass: 'fa-spin', + extraClasses: '', + icon: 'home', + markerColor: 'blue', + iconColor: 'white', + iconRotate: 0, + font: 'monospace' + }, + + initialize: function (options) { + options = L.Util.setOptions(this, options); + }, + + createIcon: function () { + var div = document.createElement('div'), + options = this.options; + + if (options.icon) { + div.innerHTML = this._createInner(); + } + + if (options.bgPos) { + div.style.backgroundPosition = + (-options.bgPos.x) + 'px ' + (-options.bgPos.y) + 'px'; + } + + this._setIconStyles(div, 'icon-' + options.markerColor); + return div; + }, + + _createInner: function() { + var iconClass, + iconSpinClass = "", + iconColorClass = "", + iconColorStyle = "", + options = this.options; + + if(!options.prefix || (options.icon.slice(0,options.prefix.length+1) === options.prefix + "-")) { + iconClass = options.icon; + } else { + iconClass = options.prefix + "-" + options.icon; + } + + if(options.spin && typeof options.spinClass === "string") { + iconSpinClass = options.spinClass; + } + + if(options.iconColor) { + if ((options.iconColor === 'white' || options.iconColor === 'black') && + options.prefix !== 'fa') { + iconColorClass = "icon-" + options.iconColor; + } else if (options.prefix === 'fa' && options.iconColor === 'white') { + iconColorClass = "fa-inverse"; + } else { + iconColorStyle = "color: " + options.iconColor + ";"; + } + } + if(options.font && options.text) { + iconColorStyle += "font-family: " + options.font + ";"; + } + + if(options.iconRotate && options.iconRotate !== 0) { + iconColorStyle += "-webkit-transform: rotate(" + options.iconRotate + "deg);"; + iconColorStyle += "-moz-transform: rotate(" + options.iconRotate + "deg);"; + iconColorStyle += "-o-transform: rotate(" + options.iconRotate + "deg);"; + iconColorStyle += "-ms-transform: rotate(" + options.iconRotate + "deg);"; + iconColorStyle += "transform: rotate(" + options.iconRotate + "deg);"; + } + + if (options.text) { + return "" + options.text + ""; + } + + return ""; + }, + + _setIconStyles: function (img, name) { + var options = this.options, + size = L.point(options[name === 'shadow' ? 'shadowSize' : 'iconSize']), + anchor; + + if (name === 'shadow') { + anchor = L.point(options.shadowAnchor || options.iconAnchor); + } else { + anchor = L.point(options.iconAnchor); + } + + if (!anchor && size) { + anchor = size.divideBy(2, true); + } + + img.className = 'awesome-marker-' + name + ' ' + options.className; + + if (anchor) { + img.style.marginLeft = (-anchor.x) + 'px'; + img.style.marginTop = (-anchor.y) + 'px'; + } + + if (size) { + img.style.width = size.x + 'px'; + img.style.height = size.y + 'px'; + } + }, + + createShadow: function () { + var div = document.createElement('div'); + + this._setIconStyles(div, 'shadow'); + return div; + } + }); + + L.AwesomeMarkers.icon = function (options) { + return new L.AwesomeMarkers.Icon(options); + }; + +}(this, document)); diff --git a/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.min.js b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.min.js new file mode 100644 index 0000000..0aaf719 --- /dev/null +++ b/_freeze/site_libs/ionicons-2.0.1/leaflet.awesome-markers.min.js @@ -0,0 +1 @@ +(function(window,document,undefined){"use strict";L.AwesomeMarkers={};L.AwesomeMarkers.version="2.0.1";L.AwesomeMarkers.Icon=L.Icon.extend({options:{iconSize:[35,45],iconAnchor:[17,42],popupAnchor:[1,-32],shadowAnchor:[10,12],shadowSize:[36,16],className:"awesome-marker",prefix:"glyphicon",spinClass:"fa-spin",extraClasses:"",icon:"home",markerColor:"blue",iconColor:"white",iconRotate:0,font:"monospace"},initialize:function(options){options=L.Util.setOptions(this,options)},createIcon:function(){var div=document.createElement("div"),options=this.options;if(options.icon){div.innerHTML=this._createInner()}if(options.bgPos){div.style.backgroundPosition=-options.bgPos.x+"px "+-options.bgPos.y+"px"}this._setIconStyles(div,"icon-"+options.markerColor);return div},_createInner:function(){var iconClass,iconSpinClass="",iconColorClass="",iconColorStyle="",options=this.options;if(!options.prefix||options.icon.slice(0,options.prefix.length+1)===options.prefix+"-"){iconClass=options.icon}else{iconClass=options.prefix+"-"+options.icon}if(options.spin&&typeof options.spinClass==="string"){iconSpinClass=options.spinClass}if(options.iconColor){if((options.iconColor==="white"||options.iconColor==="black")&&options.prefix!=="fa"){iconColorClass="icon-"+options.iconColor}else if(options.prefix==="fa"&&options.iconColor==="white"){iconColorClass="fa-inverse"}else{iconColorStyle="color: "+options.iconColor+";"}}if(options.font&&options.text){iconColorStyle+="font-family: "+options.font+";"}if(options.iconRotate&&options.iconRotate!==0){iconColorStyle+="-webkit-transform: rotate("+options.iconRotate+"deg);";iconColorStyle+="-moz-transform: rotate("+options.iconRotate+"deg);";iconColorStyle+="-o-transform: rotate("+options.iconRotate+"deg);";iconColorStyle+="-ms-transform: rotate("+options.iconRotate+"deg);";iconColorStyle+="transform: rotate("+options.iconRotate+"deg);"}if(options.text){return""+options.text+""}return""},_setIconStyles:function(img,name){var options=this.options,size=L.point(options[name==="shadow"?"shadowSize":"iconSize"]),anchor;if(name==="shadow"){anchor=L.point(options.shadowAnchor||options.iconAnchor)}else{anchor=L.point(options.iconAnchor)}if(!anchor&&size){anchor=size.divideBy(2,true)}img.className="awesome-marker-"+name+" "+options.className;if(anchor){img.style.marginLeft=-anchor.x+"px";img.style.marginTop=-anchor.y+"px"}if(size){img.style.width=size.x+"px";img.style.height=size.y+"px"}},createShadow:function(){var div=document.createElement("div");this._setIconStyles(div,"shadow");return div}});L.AwesomeMarkers.icon=function(options){return new L.AwesomeMarkers.Icon(options)}})(this,document); diff --git a/_freeze/site_libs/leaflet-easybutton-1.3.1/EasyButton-binding.js b/_freeze/site_libs/leaflet-easybutton-1.3.1/EasyButton-binding.js new file mode 100644 index 0000000..f1bfdb7 --- /dev/null +++ b/_freeze/site_libs/leaflet-easybutton-1.3.1/EasyButton-binding.js @@ -0,0 +1,44 @@ +getEasyButton = function(button) { + + var options = {}; + + options.position = button.position; + + // only add ID if provided + if(button.id) { + options.id = button.id; + } + + // if custom states provided use that + // else use provided icon and onClick + if(button.states) { + options.states = button.states; + return L.easyButton(options); + } else { + return L.easyButton(button.icon, button.onClick, + button.title, options ); + } +}; + +LeafletWidget.methods.addEasyButton = function(button) { + getEasyButton(button).addTo(this); +}; + +LeafletWidget.methods.addEasyButtonBar = function(buttons, position, id) { + + var options = {}; + + options.position = position; + + // only add ID if provided + if(id) { + options.id = id; + } + + var easyButtons = []; + for(var i=0; i < buttons.length; i++) { + easyButtons[i] = getEasyButton(buttons[i]); + } + L.easyBar(easyButtons).addTo(this); + +}; diff --git a/_freeze/site_libs/leaflet-easybutton-1.3.1/LICENSE b/_freeze/site_libs/leaflet-easybutton-1.3.1/LICENSE new file mode 100644 index 0000000..a1d3414 --- /dev/null +++ b/_freeze/site_libs/leaflet-easybutton-1.3.1/LICENSE @@ -0,0 +1,7 @@ +Copyright (C) 2014 Daniel Montague + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.css b/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.css new file mode 100644 index 0000000..18ce9ac --- /dev/null +++ b/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.css @@ -0,0 +1,56 @@ +.leaflet-bar button, +.leaflet-bar button:hover { + background-color: #fff; + border: none; + border-bottom: 1px solid #ccc; + width: 26px; + height: 26px; + line-height: 26px; + display: block; + text-align: center; + text-decoration: none; + color: black; +} + +.leaflet-bar button { + background-position: 50% 50%; + background-repeat: no-repeat; + overflow: hidden; + display: block; +} + +.leaflet-bar button:hover { + background-color: #f4f4f4; +} + +.leaflet-bar button:first-of-type { + border-top-left-radius: 4px; + border-top-right-radius: 4px; +} + +.leaflet-bar button:last-of-type { + border-bottom-left-radius: 4px; + border-bottom-right-radius: 4px; + border-bottom: none; +} + +.leaflet-bar.disabled, +.leaflet-bar button.disabled { + cursor: default; + pointer-events: none; + opacity: .4; +} + +.easy-button-button .button-state{ + display: block; + width: 100%; + height: 100%; + position: relative; +} + + +.leaflet-touch .leaflet-bar button { + width: 30px; + height: 30px; + line-height: 30px; +} diff --git a/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.js b/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.js new file mode 100644 index 0000000..579dca6 --- /dev/null +++ b/_freeze/site_libs/leaflet-easybutton-1.3.1/easy-button.js @@ -0,0 +1,370 @@ +(function(){ + +// This is for grouping buttons into a bar +// takes an array of `L.easyButton`s and +// then the usual `.addTo(map)` +L.Control.EasyBar = L.Control.extend({ + + options: { + position: 'topleft', // part of leaflet's defaults + id: null, // an id to tag the Bar with + leafletClasses: true // use leaflet classes? + }, + + + initialize: function(buttons, options){ + + if(options){ + L.Util.setOptions( this, options ); + } + + this._buildContainer(); + this._buttons = []; + + for(var i = 0; i < buttons.length; i++){ + buttons[i]._bar = this; + buttons[i]._container = buttons[i].button; + this._buttons.push(buttons[i]); + this.container.appendChild(buttons[i].button); + } + + }, + + + _buildContainer: function(){ + this._container = this.container = L.DomUtil.create('div', ''); + this.options.leafletClasses && L.DomUtil.addClass(this.container, 'leaflet-bar easy-button-container leaflet-control'); + this.options.id && (this.container.id = this.options.id); + }, + + + enable: function(){ + L.DomUtil.addClass(this.container, 'enabled'); + L.DomUtil.removeClass(this.container, 'disabled'); + this.container.setAttribute('aria-hidden', 'false'); + return this; + }, + + + disable: function(){ + L.DomUtil.addClass(this.container, 'disabled'); + L.DomUtil.removeClass(this.container, 'enabled'); + this.container.setAttribute('aria-hidden', 'true'); + return this; + }, + + + onAdd: function () { + return this.container; + }, + + addTo: function (map) { + this._map = map; + + for(var i = 0; i < this._buttons.length; i++){ + this._buttons[i]._map = map; + } + + var container = this._container = this.onAdd(map), + pos = this.getPosition(), + corner = map._controlCorners[pos]; + + L.DomUtil.addClass(container, 'leaflet-control'); + + if (pos.indexOf('bottom') !== -1) { + corner.insertBefore(container, corner.firstChild); + } else { + corner.appendChild(container); + } + + return this; + } + +}); + +L.easyBar = function(){ + var args = [L.Control.EasyBar]; + for(var i = 0; i < arguments.length; i++){ + args.push( arguments[i] ); + } + return new (Function.prototype.bind.apply(L.Control.EasyBar, args)); +}; + +// L.EasyButton is the actual buttons +// can be called without being grouped into a bar +L.Control.EasyButton = L.Control.extend({ + + options: { + position: 'topleft', // part of leaflet's defaults + + id: null, // an id to tag the button with + + type: 'replace', // [(replace|animate)] + // replace swaps out elements + // animate changes classes with all elements inserted + + states: [], // state names look like this + // { + // stateName: 'untracked', + // onClick: function(){ handle_nav_manually(); }; + // title: 'click to make inactive', + // icon: 'fa-circle', // wrapped with + // } + + leafletClasses: true, // use leaflet styles for the button + tagName: 'button', + }, + + + + initialize: function(icon, onClick, title, id){ + + // clear the states manually + this.options.states = []; + + // add id to options + if(id != null){ + this.options.id = id; + } + + // storage between state functions + this.storage = {}; + + // is the last item an object? + if( typeof arguments[arguments.length-1] === 'object' ){ + + // if so, it should be the options + L.Util.setOptions( this, arguments[arguments.length-1] ); + } + + // if there aren't any states in options + // use the early params + if( this.options.states.length === 0 && + typeof icon === 'string' && + typeof onClick === 'function'){ + + // turn the options object into a state + this.options.states.push({ + icon: icon, + onClick: onClick, + title: typeof title === 'string' ? title : '' + }); + } + + // curate and move user's states into + // the _states for internal use + this._states = []; + + for(var i = 0; i < this.options.states.length; i++){ + this._states.push( new State(this.options.states[i], this) ); + } + + this._buildButton(); + + this._activateState(this._states[0]); + + }, + + _buildButton: function(){ + + this.button = L.DomUtil.create(this.options.tagName, ''); + + if (this.options.tagName === 'button') { + this.button.setAttribute('type', 'button'); + } + + if (this.options.id ){ + this.button.id = this.options.id; + } + + if (this.options.leafletClasses){ + L.DomUtil.addClass(this.button, 'easy-button-button leaflet-bar-part leaflet-interactive'); + } + + // don't let double clicks and mousedown get to the map + L.DomEvent.addListener(this.button, 'dblclick', L.DomEvent.stop); + L.DomEvent.addListener(this.button, 'mousedown', L.DomEvent.stop); + + // take care of normal clicks + L.DomEvent.addListener(this.button,'click', function(e){ + L.DomEvent.stop(e); + this._currentState.onClick(this, this._map ? this._map : null ); + this._map && this._map.getContainer().focus(); + }, this); + + // prep the contents of the control + if(this.options.type == 'replace'){ + this.button.appendChild(this._currentState.icon); + } else { + for(var i=0;i"']/) ){ + + // if so, the user should have put in html + // so move forward as such + tmpIcon = ambiguousIconString; + + // then it wasn't html, so + // it's a class list, figure out what kind + } else { + ambiguousIconString = ambiguousIconString.replace(/(^\s*|\s*$)/g,''); + tmpIcon = L.DomUtil.create('span', ''); + + if( ambiguousIconString.indexOf('fa-') === 0 ){ + L.DomUtil.addClass(tmpIcon, 'fa ' + ambiguousIconString) + } else if ( ambiguousIconString.indexOf('glyphicon-') === 0 ) { + L.DomUtil.addClass(tmpIcon, 'glyphicon ' + ambiguousIconString) + } else { + L.DomUtil.addClass(tmpIcon, /*rollwithit*/ ambiguousIconString) + } + + // make this a string so that it's easy to set innerHTML below + tmpIcon = tmpIcon.outerHTML; + } + + return tmpIcon; +} + +})(); diff --git a/index.qmd b/index.qmd index 3b2e8b6..00365b8 100644 --- a/index.qmd +++ b/index.qmd @@ -4,7 +4,7 @@ title:
    Rochester Region Federal Funds Dashboard" subtitle: "How the Rochester Region Is Spending Federal Recovery Dollars" -date: '`r readxl::read_xlsx("data/data-raw/dashboard-data.xlsx") |> dplyr::pull("current_as_of") |> unique() |> stringr::str_remove(" UTC") |> stringr::str_replace_all("-", "/")`' +date: '`r readxl::read_xlsx("data/data-raw/dashboard-data.xlsx") |> dplyr::pull("current_as_of") |> unique() |> stringr::str_remove(" UTC") |> stringr::str_replace_all("-", "/") |> lubridate::ymd() |> format("%m/%d%/%y") `' date-format: "MMMM D, YYYY" language: title-block-published: "Data as of" @@ -26,8 +26,6 @@ knitr: # knitr::include_graphics(here::here("www", "images", "act-logo.png"), dpi = 800) ``` - - ```{r} ragg_png = function(..., res = 192) { ragg::agg_png(..., res = res, units = "in") @@ -46,11 +44,15 @@ librarian::shelf(tidyverse, leaflet.extras2, leaflet.providers, leafpop, + sf, tmap, ggtext, + boxr, + epoxy, htmltools) options(tigris_use_cache = TRUE) set_urbn_defaults(style = "print", base_size = 12) +box_auth() racf_palette <- c("#D6A123", "#ED0A72", "#D442CF", "#696B4F", "#8FAEBE") ``` @@ -66,53 +68,108 @@ data <- mutate(racf_categories = str_to_title(racf_categories)) %>% select(-c(matches("notes"), expiration, is_expired, notes, summary_description, flexible_fund, amount_exact)) -current_as_of <- distinct(data, current_as_of) %>% pull() %>% stringr::str_remove(" UTC") %>% stringr::str_replace_all("-", "/") + source <- glue::glue('Source: Urban Institute calculations of data from {tags$a("City of Rochester ARPA Reporting Dashboard", href = "https://app.powerbi.com/view?r=eyJrIjoiMmFkYzI3ZTAtMjIzMS00YWQ0LTgyZjItYmY5YTIxYTIxNzUwIiwidCI6ImJlMjdkYTJlLTk0YjQtNDFkYS05NDdkLWY2MDJkNjBlODA3YyJ9&pageName=ReportSectioneb9f6e0e0a6d0db032dc")} and {tags$a("Monroe County Recovery Plan 2023 Annual Report", href = "https://www.monroecounty.gov/files/planning/arpa/July_2023_ARPA_Report_V3_8-22-23_With_Attachments.pdf")}') ``` +```{r} +pct <- + data %>% + group_by(is_building_block = !is.na(building_blocks)) %>% + summarise(allocation = sum(allocation, na.rm = TRUE)) %>% + mutate(total = sum(allocation), + pct= allocation / total) %>% + filter(is_building_block == TRUE) %>% + pull(pct) %>% + scales::label_percent(digits = 1)(.) -In response to the economic impacts of the COVID-19 pandemic, the federal government provided unprecedented levels of flexible relief funds to local governments across the country, [including Rochester](https://www.urban.org/sites/default/files/publication/105119/harnessing-federal-funds-for-inclusive-recovery-in-rochester-ny_0.pdf) -. These federal recovery funds [offer an unparalleled opportunity](https://www.urban.org/research/publication/how-equity-helps-lift-all-boats) to not only stimulate economic recovery from the pandemic, but to also increase equity. To do so, the funds must be used to ensure that everyone, especially historically excluded groups, can benefit from and contribute to economic recovery. [Research](https://www.urban.org/research/publication/inclusive-recovery-us-cities) has demonstrated that cities can recover from economic distress while improving on inclusion and equity. However, this is not a given. Many cities lose ground on either racial or economic inclusion during a recovery by allowing funds to flow to business as usual. - -To increase transparency in this process, this dashboard shares data on how the City of Rochester and Monroe County allocated and spent a portion of the flexible recovery funds. The dashboard focuses on [State and Local Fiscal Recovery Funds (SLFRF)](https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments/state-and-local-fiscal-recovery-funds#:~:text=The%20Coronavirus%20State%20and%20Local,COVID%2D19%20public%20health%20emergency) provided through the American Rescue Plan Act (ARPA) because they were one of the most flexible funding sources, giving local governments authority to decide how they should be spent. And, they tended to be spent more on longer term recovery efforts than on short term emergency public health response, which was the focus of CARES Act (Coronavirus Aid, Relief, and Economic Security Act) dollars. SLFRF must be obligated by the end of 2024 and spent by the end of 2026. - -The dashboard starts by showing what percent of the allocations align with the building blocks of an inclusive recovery, which are five strategies for spending that direct funds to support equity and inclusion. As of `r current_as_of` date, 61% of the dollars in the City of Rochester and Monroe County were allocated toward programs and initiatives that align with these building blocks. The dashboard then shows more broadly how funds are being spent by policy area and subtopic. Next, it explores how well the allocations align with Rochester Area Community Foundation’s priorities. Finally, it shows how the City of Rochester’s SLFRF capital investments map onto racial characteristics of neighborhoods within the region, as well as how they overlap with the original redlining maps for the city. (Redlining refers to the process in which borrowers are denied access to credit due to the racial composition of their neighborhood.) Finally, at the end of the dashboard is a [table](#programs-table). showing all of the allocations and their descriptions in detail. - -Note that this dashboard does not track outcomes from these investments – it only shows where the dollars are flowing. Simply allocating funds toward topics that could increase equity and inclusion does not guarantee that they do so. Future research efforts should closely monitor outcomes and impacts from the recovery dollar investments to ensure that they close equity gaps exacerbated by the pandemic and address the root causes of inequities. - +last_update_date <- + readxl::read_xlsx("data/data-raw/dashboard-data.xlsx") |> dplyr::pull("current_as_of") |> unique() |> stringr::str_remove(" UTC") %>% as_date() %>% format("%m/%d%/%y") +``` + +The federal government provided unprecedented levels of flexible relief funds to +local governments across the country, [including those in the Rochester +region](https://www.urban.org/sites/default/files/publication/105119/harnessing-federal-funds-for-inclusive-recovery-in-rochester-ny_0.pdf), +to help them recover from the economic impacts of the COVID-19 pandemic. As the +City of Rochester and Monroe County continue to allocate and spend these federal +recovery funds, they have an unparalleled opportunity to not only stimulate +economic recovery from the pandemic, but to also increase equity and remediate +some of the past inequities that have been [built into our nation's +laws](https://books.google.com/books?hl=en&lr=&id=SdtDDQAAQBAJ&oi=fnd&pg=PT4&dq=color+of+law&ots=RL_u4QqRBH&sig=eYYnHvwibwB_XBfpzr0jQVK2SOs#v=onepage&q=color%20of%20law&f=false) +and programs. To do so, the funds [must be +used](https://www.urban.org/research/publication/inclusive-recovery-us-cities) +to ensure that everyone--especially historically excluded groups--can benefit +from and contribute to economic growth. + +To increase transparency in this process, this dashboard tracks data on how the +City of Rochester and Monroe County have allocated and spent a portion of these +recovery funds: [State and Local Fiscal Recovery +Funds](https://home.treasury.gov/policy-issues/coronavirus/assistance-for-state-local-and-tribal-governments/state-and-local-fiscal-recovery-funds#:~:text=The%20Coronavirus%20State%20and%20Local,COVID%2D19%20public%20health%20emergency) +(SLFRF) provided through the American Rescue Plan Act (ARPA). SLFRF must be +obligated by the end of 2024 and spent by the end of 2026. The dashboard focuses +on SLFRF because of their flexibility in giving local governments authority to +decide how they should be spent. And, they tended to be on longer term recovery +efforts rather than on short term emergency public health response, which was +the focus of Coronavirus Aid, Relief, and Economic Security Act (CARES Act) +dollars. + +The dashboard starts by showing what percent of the allocations align with the +building blocks of an inclusive recovery, which are five strategies for spending +that direct funds to support equity and inclusion. These five building blocks +are: create jobs for those who face the greatest barriers to employment, connect +historically excluded residents to jobs and economic opportunities, reinvest in +disinvested communities, stabilize housing and expand affordable housing, and +create opportunities for low-wealth households to build wealth. As of +`r last_update_date`, `r pct` of the dollars in the City of Rochester and Monroe +County were allocated toward programs and initiatives that align with these +building blocks. + +The dashboard then shows more broadly how funds are being spent by policy area +and subtopic. Next, it explores how well the allocations align with Rochester +Area Community Foundation's priorities. Finally, it shows how the City of +Rochester's SLFRF capital investments map onto racial characteristics of +neighborhoods within the region, as well as how they overlap with the original +redlining maps for the city. (Redlining refers to the process in which borrowers +are denied access to credit due to the racial composition of their +neighborhood.) At the end of the dashboard is a table showing all of the +allocations and their descriptions in detail. + +Note that this dashboard does not track outcomes from these investments -- it +only shows where the dollars are flowing. Simply allocating funds toward topics +that could increase equity and inclusion does not guarantee that they do so. +Future research efforts should closely monitor outcomes and impacts from the +recovery dollar investments to ensure that they close equity gaps exacerbated by +the pandemic and address the root causes of inequities. ## How Much is Being Spent on an Inclusive Recovery? -[Previous work by the Urban Institute](https://www.urban.org/sites/default/files/publication/105290/aligning-the-use-of-recovery-funds-with-community-goals-in-rochester-new-york.pdf) identified five building blocks of an inclusive recovery. These building blocks were created in collaboration with community stakeholders across the country and include: +[Previous work by the Urban +Institute](https://www.urban.org/sites/default/files/publication/105290/aligning-the-use-of-recovery-funds-with-community-goals-in-rochester-new-york.pdf) +identified five building blocks of an inclusive recovery. These building blocks +were created in collaboration with community stakeholders across the country and +include: 1. **Create jobs** for residents hardest hit by the pandemic or who face the greatest barriers to employment 2. **Connect residents to jobs** and economic opportunities, including through workforce development, child care, transportation, or broadband -3. **Reinvest in disinvested communities** and address long-standing disparities in - access to education, capital, economic opportunities, and climate resilience +3. **Reinvest in disinvested communities** and address long-standing + disparities in access to education, capital, economic opportunities, and + climate resilience 4. **Stabilize housing and expand affordable housing** options for low-income households and housing-insecure renters 5. Create opportunities for low-wealth households to **build wealth**. -```{r} -pct <- - data %>% - filter(geography == "Rochester") %>% - group_by(is_building_block = !is.na(building_blocks)) %>% - summarise(allocation = sum(allocation, na.rm = TRUE)) %>% - mutate(total = sum(allocation), - pct= allocation / total) %>% - filter(is_building_block == TRUE) %>% - pull(pct) %>% - scales::label_percent()(.) -``` -Across all allocations in the Rochester region, **`r pct`** of the funding has been allocated towards these strategies that promote inclusive recovery and equitable growth. The figure below shows the total amount of funding for Rochester and Monroe County that was allocated to programs that align with these building blocks. - +Across all allocations for the city and county, **`r pct`** of the funding has +been allocated towards these strategies that promote inclusive recovery and +equitable growth. The figure below shows the total amount of funding for +Rochester and Monroe County that was allocated to programs that align with these +building blocks, based on review of the program description and goals. ```{r} + #| fig-cap: !expr source data %>% @@ -137,7 +194,7 @@ data %>% name = "Allocation" ) + scale_alpha_manual( - values = c("Allocated to a building block" = 1, "Allocated to other programs" = 0.5), + values = c("Allocated to a building block" = 1, "Allocated to other programs" = 1), guide = "none" ) + theme_void() @@ -146,8 +203,13 @@ data %>% ## What types of inclusive recovery strategies are supported? - -Out of the funds allocated to programs that align with the five building blocks of an inclusive recovery, the most funding has been allocated to reinvesting in disinvested communities, while the least has been allocated to creating jobs for residents hardest hit by the pandemic or who face the greatest barriers to employment. The figure below shows how that funding is divided among the five building blocks and includes only the funds allocated to programs that align with the building blocks. +Out of the funds allocated to programs that align with the five building blocks +of an inclusive recovery, the most funding has been allocated to reinvesting in +disinvested communities, while the least has been allocated to creating jobs for +residents hardest hit by the pandemic or who face the greatest barriers to +employment. The figure below shows how that funding is divided among the five +building blocks and includes only the funds allocated to programs that align +with the building blocks. ```{r} #| fig-cap: !expr source @@ -184,56 +246,51 @@ data_reordered %>% labs(x = NULL, y = 'Total Allocation') ``` - - - ## What policy areas are being funded? -Zooming out, overall, $286M of SLFRF funds have been allocated to the City of Rochester and Monroe County, and 8% have been reported as spent. The figure below shows how that funding has been allocated across policy categories and includes all allocations, not just those that align with the building blocks of an inclusive recovery. To view individual project allocations and their policy categories, see the funding table at the end of the dashboard. - -By category, most of the funds have been allocated to Community and Economic Development, Infrastructure, and Housing, while the least have been allocated to Operations, Public Safety, and Social Services. - ```{r} -total <- summarise(data, total = sum(allocation, na.rm = TRUE)) %>% pull() -total_fmt <- scales::label_number(scale = 1/1e6, suffix = 'M')(total) +total_allocated <- summarise(data, total = sum(allocation, na.rm = TRUE)) %>% pull() total_spent <- summarise(data, total_spent = sum(spent, na.rm = TRUE)) %>% pull() -total_spent_fmt <-scales::label_number(scale = 1/1e6, suffix = 'M')(total_spent) -pct_spent <- total_spent / total -pct_spent_fmt <- scales::label_percent()(pct_spent) -``` +pct_spent <- total_spent / total_allocated -```{r} -total_allocations <- data %>% - summarise(total_allocation = sum(allocation, na.rm = TRUE), .by = 'topic') %>% - arrange(desc(total_allocation)) +allocations_by_topic <- + data %>% + summarise(topic_allocation = sum(allocation, na.rm = TRUE), .by = 'topic') %>% + arrange(desc(topic_allocation)) top3 <- - total_allocations %>% - slice_max(total_allocation, n = 3) %>% + allocations_by_topic %>% + slice_max(topic_allocation, n = 3) %>% pull(topic) %>% str_flatten_comma(", and ") bottom3 <- - total_allocations %>% - slice_min(total_allocation, n = 3) %>% + allocations_by_topic %>% + slice_min(topic_allocation, n = 3) %>% pull(topic) %>% str_flatten_comma(", and ") ``` +```{epoxy, .transformer = epoxy_transform_inline(.percent = scales::label_percent(accuracy = 0.1), .dollar = scales::label_dollar(scale = 1/1e6, suffix = "M", accuracy = 1))} + + +The City of Rochester and Monroe County have allocated almost **{.dollar total_allocated}** of SLFRF funds, and, of those, **{.pct pct_spent}** have been reported as spent. The figure below shows how that funding has been allocated across policy categories and includes all allocations, not just those that align with the building blocks of an inclusive recovery. + +By category, most of the funds have been allocated to {top3}, while the least have been allocated to {bottom3}. -::: panel-tabset +``` +::: panel-tabset ## Allocated ```{r} #| fig-cap: !expr source data_reordered <- data %>% - left_join(total_allocations, by = "topic") %>% - mutate(topic = reorder(topic, total_allocation)) %>% - summarise(allocation = sum(allocation, na.rm = TRUE), .by = c("topic", "geography")) %>% - drop_na() + left_join(allocations_by_topic, by = "topic") %>% + mutate(topic = reorder(topic, topic_allocation)) %>% + summarise(allocation = sum(allocation, na.rm = TRUE), .by = c("topic", "geography")) # Plotting the graph ggplot(data_reordered, aes(x = topic, y = allocation, fill = geography)) + @@ -276,15 +333,16 @@ data %>% scale_fill_manual(values = racf_palette) ``` - ::: ## What subtopics are funded within each policy area? -The figure below provides a more detailed breakdown of allocations to each policy area. Each policy area tab breaks down the allocations into subtopics. For example, most of the money allocated to community and economic development has gone to the workforce development subtopic. To view individual project allocations and their subtopic categories, see the funding table at the end of the dashboard. +The figure below provides a more detailed breakdown of allocations to each +policy area. Each policy area tab breaks down the allocations into subtopics. +For example, most of the money allocated to community and economic development +has gone to workforce development. ::: panel-tabset - ### Community and Economic Development ```{r} @@ -302,87 +360,80 @@ data %>% labs(x = NULL, y = "Total allocation") ``` - -### Housing +### Infrastructure ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Housing")) %>% + filter(str_detect(topic, "Infrastructure")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1/1e6, suffix = 'M', prefix = '$'), limits = c(0, 50 * 1e6)) + coord_flip() + - scale_fill_manual(values = racf_palette) + - scale_x_discrete(limits = rev) + + scale_fill_manual(values = racf_palette) + + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` - -### Infrastructure - +### Housing ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Infrastructure")) %>% + filter(str_detect(topic, "Housing")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1/1e6, suffix = 'M', prefix = '$'), limits = c(0, 50 * 1e6)) + coord_flip() + - scale_fill_manual(values = racf_palette) + - scale_x_discrete(limits = rev) + + scale_fill_manual(values = racf_palette) + + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` - -### Operations +### Public Safety ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Operations")) %>% + filter(str_detect(topic, "Public Safety")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1/1e6, suffix = 'M', prefix = '$'), limits = c(0, 50 * 1e6)) + coord_flip() + - scale_x_discrete(limits = rev) + scale_fill_manual(values = racf_palette) + + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` - -### Public Health +### Social Services ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Public Health")) %>% + filter(str_detect(topic, "Social Services")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1/1e6, suffix = 'M', prefix = '$'), limits = c(0, 50 * 1e6)) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual(values = racf_palette) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` - - -### Public Safety +### Public Health ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Public Safety")) %>% + filter(str_detect(topic, "Public Health")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + @@ -394,49 +445,58 @@ data %>% labs(x = NULL, y = "Total allocation") ``` -### Social Services +### Operations ```{r} #| fig-cap: !expr source data %>% - filter(str_detect(topic, "Social Services")) %>% + filter(str_detect(topic, "Operations")) %>% summarise(allocated = sum(allocation, na.rm = TRUE), .by = c('subtopic', 'geography')) %>% ggplot(aes(x = subtopic, y = allocated, fill = geography)) + geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1/1e6, suffix = 'M', prefix = '$'), limits = c(0, 50 * 1e6)) + coord_flip() + - scale_fill_manual(values = racf_palette) + scale_x_discrete(limits = rev) + + scale_fill_manual(values = racf_palette) + labs(x = NULL, y = "Total allocation") ``` - ::: -## How does funding align with the Rochester Area Community Foundation’s priorities? - -The Rochester Area Community Foundation’s key priorities for investment in the region include closing the academic achievement and opportunity gap, fostering racial and ethnic understanding and equity, partnering against poverty, supporting arts and culture, preserving historic assets, advancing environmental justice and sustainability, and promoting successful aging. Overall, 43% of SLFRF funding has been allocated towards these priority areas. The figure below shows how SLFRF funding is divided among the priority topics and includes only the funds allocated to programs that align with one of RACF’s priority areas. +## How does funding align with the Rochester Area Community Foundation's priorities? -```{r} -pct <- +```{r racf-funding-pct} +allocated_to_racf_pct <- data %>% - filter(geography == "Rochester") %>% group_by(is_priority = is.na(racf_categories)) %>% summarise(allocation = sum(allocation, na.rm = TRUE)) %>% - mutate(total = sum(allocation), + mutate(total = sum(allocation, na.rm = TRUE), pct = allocation / total) %>% filter(is_priority == TRUE) %>% - pull(pct) %>% - scales::label_percent()() + pull(pct) ``` - The Rochester Area Community Foundation's key priorities for investment in the -region include closing the academic achievement and opportunity gap, fostering -racial and ethnic understanding and equity, partnering against poverty, -supporting arts and culture, preserving historic assets, advancing environmental -justice and sustainability, and promoting successful aging. Overall, **`r pct`** of the -Federal funding has been allocated towards these priority areas: +region include: + +- Closing the academic achievement and opportunity gap; + +- Fostering racial and ethnic understanding and equity; + +- Partnering against poverty; + +- Supporting arts and culture; Preserving historic assets; + +- Advancing environmental justice and sustainability; and + +- Promoting successful aging. + +```{epoxy, .transformer = epoxy_transform_inline(.percent = scales::label_percent(accuracy = 0.1))} + +Overall, **{.pct allocated_to_racf_pct}** of SLFRF funding has been allocated towards these priority areas. The figure below shows how SLFRF funding is divided among the priority topics and includes only the funds allocated to programs that align with one of RACF's priority areas. + + +``` ```{r} #| fig-cap: !expr source @@ -455,35 +515,56 @@ data %>% labs(x = NULL, y = 'Total Allocation') ``` - -## Which neighborhoods are the funds being spent in? - -The map below shows how the City of Rochester’s SLFRF capital investments map onto racial characteristics of neighborhoods, as well as the original Federal redlining maps for the city. Click on the tabs at the top to see how they overlap with the percent of residents who are Black in a neighborhood and the percent of residents who are Latine in a neighborhood. - -Redlining refers to the system that the Federal Housing Administration and the Home Owners’ Loan Corporation used to grade the profitability of neighborhoods in the late 1930s. The four categories were green (“best”), blue (“still desirable”), yellow (“definitely declining”), and red (“hazardous”). These grades were largely based on the neighborhood’s racial, ethnic, socioeconomic, and religious composition. Generally, White, middle-class neighborhoods received FHA home loans, whereas many Black and Latine neighborhoods were deemed hazardous and declining in value and did not receive FHA insured mortgages or loans. These maps [had long lasting effects](https://www.econstor.eu/bitstream/10419/200568/1/1010730592.pdf) on racial segregation, homeownership, and house values in redlined neighborhoods. - -To increase equity in the region, leaders must overinvest in communities that have been underinvested in historically. These investments must be those that [benefit incumbent residents](https://www.urban.org/research/publication/what-policies-and-programs-can-support-inclusive-recovery-rochester-new-york), meaning that they should not be investments that only benefit new, incoming wealthier residents, or those that have negative impacts on immediate neighbors, like a sewage treatment facility or a highway that pollutes the air. And, they must be investments that [community members want](https://www.urban.org/research/publication/aligning-use-recovery-funds-community-goals-rochester-new-york), particularly historically excluded members. The maps below give us a first approximation of how equitably investments are distributed, but further exploration of how these investments align with the goals of community members and how much they benefit historically excluded residents is necessary to ensure that they are increasing equity within those communities. +## Which neighborhoods are the funds being spent in? + +The map below shows how the City of Rochester's SLFRF capital investments map +onto racial characteristics of neighborhoods, as well as the original Federal +redlining maps for the city. Click on the tabs at the top to see how they +overlap with the percent of residents who are Black in a neighborhood and the +percent of residents who are Hispanic/Latino[^1]. + +[^1]: We use the term "Hispanic/Latino" in this map because this is the term + used in the data collected by the American Community Survey. We recognize + that this may not be the preferred identifier for all, and we remain + committed to employing inclusive language whenever possible.  + +[Redlining](https://www.npr.org/2017/05/03/526655831/a-forgotten-history-of-how-the-u-s-government-segregated-america) +refers to the system that the Federal Housing Administration and the Home +Owners' Loan Corporation used to grade the profitability of neighborhoods in the +late 1930s. The four categories were green ("best"), blue ("still desirable"), +yellow ("definitely declining"), and red ("hazardous"). These grades were +largely based on the neighborhood's racial, ethnic, socioeconomic, and religious +composition. Generally, White, middle-class neighborhoods received FHA home +loans, whereas many Black and Hispanic/Latino neighborhoods were deemed +hazardous and declining in value and did not receive FHA insured mortgages or +loans. These maps [had long lasting +effects](https://www.econstor.eu/bitstream/10419/200568/1/1010730592.pdf) on +racial segregation, homeownership, and house values in redlined neighborhoods. + +To increase equity in the region, leaders must overinvest in communities that +have been underinvested in historically. These investments must be those that +[benefit incumbent +residents](https://www.urban.org/research/publication/what-policies-and-programs-can-support-inclusive-recovery-rochester-new-york), +meaning that they should not be investments that only benefit new, incoming +wealthier residents, or those that have negative impacts on immediate neighbors, +like a sewage treatment facility or a highway that pollutes the air. And, they +must be investments that [community members +want](https://www.urban.org/research/publication/aligning-use-recovery-funds-community-goals-rochester-new-york), +particularly historically excluded members. The maps below give us a first +approximation of how equitably investments are distributed, but further +exploration of how these investments align with the goals of community members +and how much they benefit historically excluded residents is necessary to ensure +that they are increasing equity within those communities.  ::: panel-tabset - -### Redlining - -```{r} -redlining_source <- glue::glue('Source: {tags$a("The Mapping Inequality Project", href = "https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58")} and ACS Data 2017-2021') - -``` - -```{r} +```{r redlining-data-wrangling} #|fig-cap: !expr redlining_source -pacman::p_load(sf, mapview, tidycensus) +redlining_source <- glue::glue('Source: {tags$a("The Mapping Inequality Project", href = "https://dsl.richmond.edu/panorama/redlining/#loc=5/39.1/-94.58")} and ACS Data 2017-2021') redlining <- sf::read_sf("data/data-raw/redlining.geojson.txt") %>% filter(city == 'Rochester', state == 'NY') - - - spending <- readxl::read_xlsx("data/data-raw/City of Rochester ARPA capital projects for ACT Rochester.xlsx", sheet = "ARPA spending status", @@ -495,6 +576,9 @@ spending <- rename_with(~str_to_title(.x) %>% str_replace_all("_", " "), everything()) %>% rename(`ARPA Funding` = `Arpa funding`) +genesee_spending <- + spending %>% filter(str_detect(`Street`,"873|Genesee")) + project_locations <- read_csv("data/data-raw/addresses-geocoded.csv") %>% janitor::clean_names() %>% @@ -502,7 +586,77 @@ project_locations <- sf::st_as_sf(coords = c('longitude', 'latitude'), crs = 4326) %>% st_transform(st_crs(4326)) %>% filter(type == "arpa_spending_status") %>% - left_join(spending, by = c("f_address" = "Street")) + distinct(match_addr, .keep_all = TRUE) %>% + left_join(spending, by = c("f_address" = "Street")) %>% + mutate(`Project name` = if_else(str_detect(match_addr, "873|Genesee"), genesee_spending$`Project name`, `Project name`), + `Description` = if_else(str_detect(match_addr, "873|Genesee"), genesee_spending$`Description`, `Description`), + `ARPA Funding` = if_else(str_detect(match_addr, "873|Genesee"), genesee_spending$`ARPA Funding`, `ARPA Funding`), + `Arpa funding numeric` = if_else(str_detect(match_addr, "873|Genesee"), genesee_spending$`Arpa funding numeric`, `Arpa funding numeric`)) %>% + drop_na(`Project name`) + + +``` + +```{r race-data-wrangling, echo=FALSE, message=FALSE, warning=FALSE,error=FALSE} +rochester <- + tigris::places("NY", cb = TRUE, year = 2021) %>% + filter(NAME == "Rochester") %>% + sf::st_geometry() + +race_vars <- c( + black = "B03002_004", + hispanic = "B03002_012" + ) + +race <- tidycensus::get_acs( + geography = "tract", + state = "NY", + variables = race_vars, + summary_var = "B03002_001", + year = 2021, + geometry = TRUE + ) %>% + st_filter(rochester, .predicate = st_intersects) %>% + as_tibble() %>% + janitor::clean_names() %>% + select(geoid, total_population = summary_est, race = variable, value = estimate, geometry) %>% + summarise(value = sum(value, na.rm = TRUE), + percent = value/ total_population, + .by = c("geoid", "race", "total_population", "geometry")) %>% + select(-c(total_population, value)) %>% + pivot_wider(names_from = 'race', values_from = 'percent', names_prefix = "pct_") %>% + sf::st_as_sf() + + +``` + +```{r project-locations-map} +my_popup <- + glue::glue("
    {project_locations$`Project name`}
    ", + "ARPA Funding: {project_locations$`ARPA Funding`}
    ", + "Description: {project_locations$`Description`}
    ") %>% + map(htmltools::HTML) + + +my_label <- glue::glue("{project_locations$`Project name`} ({project_locations$`ARPA Funding`})") %>% + map(htmltools::HTML) + +project_location_map <- + mapview(project_locations %>% + mutate(type = if_else(type == "arpa_spending_status", "City of Rochester Capital Projects", type)), + zcol = 'type', cex = "Arpa funding numeric", col.regions = "grey", + popup = my_popup, + label = my_label, + legend = TRUE, + layer.name = "Project type") # Use blue to red color scale for project types + +``` + + +### Redlining + +```{r redlining-map} + # Create the map m <- mapviewOptions(fgb = TRUE) # This will make the base map faster by using a faster background map @@ -515,14 +669,6 @@ m <- row.numbers = FALSE), col.regions = colorRampPalette(redlining_palette), legend = TRUE, layer.name = "Redlining grade") -project_location_map <- mapview(project_locations %>% mutate(type = if_else(type == "arpa_spending_status", "City of Rochester Capital Projects", type)), zcol = 'type', cex = "Arpa funding numeric", col.regions = "grey", - popup = popupTable(project_locations, - zcol = c("Project name", - "ARPA Funding", - "Description"), - feature.id = FALSE, - row.numbers = FALSE - ), legend = TRUE, layer.name = "Project type") # Use blue to red color scale for project types m + project_location_map ``` @@ -551,140 +697,97 @@ make_redlining_map <- function(data){ tm_shape(name = "Project type") + tm_dots(col = "type", id = "Project name", alpha = 0.9, + border.alpha = 0.5, + style="fixed", + contrast = 1, title = expr("Project type"), palette = palette_urbn_gray[[5]], size = "arpa_funding", - popup.vars = c("ARPA funding"="arpa_funding"), + popup.vars = c("Project name", "ARPA funding"="arpa_funding", "Description"), popup.format = list(fun = scales::label_dollar(scale = 1/1e6, accuracy = 1))) tmap_redlining + tmap_projects } -make_redlining_map(redlining) -``` +m1 <- make_redlining_map(redlining) + tm_view(set.view = 12) -### Percent Black +m1 -```{r get-race, echo=FALSE, message=FALSE, warning=FALSE,error=FALSE} -rochester <- - tigris::places("NY", cb = TRUE, year = 2021) %>% - filter(NAME == "Rochester") %>% - st_geometry() -race_vars <- c( - black = "B03002_004", - hispanic = "B03002_012" - ) +``` -race <- tidycensus::get_acs( - geography = "tract", - state = "NY", - variables = race_vars, - summary_var = "B03002_001", - year = 2021, - geometry = TRUE - ) %>% - st_filter(rochester, .predicate = st_intersects) %>% - as_tibble() %>% - janitor::clean_names() %>% - select(geoid, total_population = summary_est, race = variable, value = estimate, geometry) %>% - summarise(value = sum(value, na.rm = TRUE), - percent = value/ total_population, - .by = c("geoid", "race", "total_population", "geometry")) %>% - select(-c(total_population, value)) %>% - pivot_wider(names_from = 'race', values_from = 'percent', names_prefix = "pct_") %>% - st_as_sf() +### Percent Black -mapview(select(race, geoid, pct_black), +```{r pct-black-map} +mapviewOptions(fgb = FALSE) +black_map <- + mapview(select(race, geoid, pct_black), zcol = "pct_black", layer.name = "Percent Black", + label = scales::label_percent(accuracy = 1)(race$pct_black), col.regions = colorRampPalette(palette_urbn_cyan), - popup = popupTable(select(race %>% st_drop_geometry(), geoid, `Percent Black` = pct_black) %>% - mutate(`Percent Black` = scales::label_percent(accuracy = 0.1)(`Percent Black`)), row.numbers = FALSE, feature.id = FALSE)) + - project_location_map - - -``` - - -```{r} -#| include: false -tmap_mode("view") - -map_population <- function(data, var, title, palette){ - - - - tmap_population <- - data %>% - select(geoid, {{var}}) %>% - tm_shape(name = title) + - tm_polygons(col = var, title.col =if_else(var == "pct_black", c("Percent Black"="pct_black"), c("Percent Latine"="pct_hispanic")), palette = palette, - popup.vars= if_else(var == "pct_black", c("Percent Black"="pct_black"), c("Percent Latine"="pct_hispanic")), - id = "geoid", - title = title, - popup.format= list(fun = scales::label_percent(accuracy = 0.1), list(digits=2)), - legend.format = list(fun = scales::label_percent(accuracy = 1))) - -tmap_projects <- - project_locations %>% - mutate(type = str_replace_all(type, "_"," ") %>% str_to_sentence() %>% str_replace("Arpa", "ARPA")) %>% - rename(arpa_funding = `Arpa funding numeric`) %>% - tm_shape(name = "Project type") + - tm_dots(col = "type", id = "Project name", - title = expr("Project type"), - palette = palette_urbn_gray[[3]], - size = "arpa_funding", - popup.vars = c("ARPA funding"="arpa_funding"), - popup.format = list(fun = scales::label_dollar(scale = 1/1e6, accuracy = 1)), - ) - -tmap_population + tmap_projects - -} - + popup = popupTable(select(race %>% st_drop_geometry(), + geoid, `Percent Black` = pct_black) %>% + mutate(`Percent Black` = scales::label_percent(accuracy = 0.1)(`Percent Black`)), + row.numbers = FALSE, feature.id = FALSE), + highlight = highlightOptions( + weight = 2, + color = "#666", + fillOpacity = 0.7, + bringToFront = TRUE + ), + map.types = "CartoDB.Positron", + legend = TRUE) + + black_map + project_location_map ``` +### Percent Hispanic/Latino -```{r} -#|eval: false -#|include: false -map_population(race, "pct_black", "Percent Black", palette_urbn_cyan) - -``` - - -### Percent Latine - -```{r} -#| fig-cap: "**Note:** This dashboard uses the term “Latine” because some consider it a more inclusive term than “Hispanic,” because it includes people from all countries in Latin America, and than “Latino/a” because it is a gender neutral term. Percentages shown in this map were based on American Community Survey data for “Hispanic or Latino” population." -#|eval: false -#|include: false - -map_population(race, "pct_hispanic", "Percent Latine", palette_urbn_green) - -``` -```{r} -mapview(select(race, geoid, pct_hispanic), +```{r pct-hispanic-map} +mapviewOptions(fgb = FALSE) +hispanic_map <- + mapview(select(race, geoid, pct_hispanic), zcol = "pct_hispanic", - layer.name = "Percent Latine", + label = scales::label_percent(accuracy = 1)(race$pct_hispanic), + layer.name = "Percent Hispanic or Latino", col.regions = colorRampPalette(palette_urbn_green), - popup = popupTable(select(race %>% st_drop_geometry(), geoid, `Percent Latine` = pct_hispanic) %>% - mutate(`Percent Latine` = scales::label_percent(accuracy = 0.1)(`Percent Latine`)), row.numbers = FALSE, feature.id = FALSE)) + - project_location_map + popup = popupTable(select(race %>% st_drop_geometry(), + geoid, `Percent Hispanic/Latino` = pct_hispanic) %>% + mutate(`Percent Hispanic/Latino` = scales::label_percent(accuracy = 0.1)(`Percent Hispanic/Latino`)), + row.numbers = FALSE, feature.id = FALSE), + highlight = highlightOptions( + weight = 2, + color = "#666", + fillOpacity = 0.7, + bringToFront = TRUE + ), + map.types = "CartoDB.Positron", + legend = TRUE) + + hispanic_map + project_location_map ``` - :::
    - ## Explore All Programs Funded {#programs-table} -This table shows all programs that the City of Rochester and Monroe County have allocated SLFRF funds to, as well as their building block, policy area, policy subtopic, and RACF priority area category. The default display on the table shows the programs in order from largest to smallest allocation. You can use the arrows next to each column title to sort the table by that column, and you can use the search bars under each column or the overall search bar to search the table. + +This table shows all programs that the City of Rochester and Monroe County have +allocated SLFRF funds to, as well as their building block, policy area, policy +subtopic, and RACF priority area category. The default display on the table +shows the programs in order from largest to smallest allocation. You can use the +arrows next to each column title to sort the table by that column, and you can +use the search bars under each column or the overall search bar to search the +table.  + +Note: Data on spending were only available from the City of Rochester  + +Data current as of: `r last_update_date` ```{r} #|label: "programs-table" @@ -746,9 +849,18 @@ data %>% ## About the Dashboard -This dashboard was created by the [Urban Institute](https://www.urban.org/) in partnership with and support from [The Rochester Area Community Foundation (RACF)](https://www.racf.org/) to visualize Monroe County and the City of Rochester’s ARPA spending by the five building blocks of inclusive recovery, policy category, and RACF’s investment priorities. By tracking recovery funding expenditures, this dashboard allows us to monitor public spending by the categories most critical in supporting an inclusive recovery from the COVID-19 pandemic. - +This dashboard was created by the [Urban Institute](https://www.urban.org/) in +partnership with and support from [The Rochester Area Community Foundation +(RACF)](https://www.racf.org/) to visualize Monroe County and the City of +Rochester's ARPA spending by the five building blocks of inclusive recovery, +policy category, and RACF's investment priorities. By tracking recovery funding +expenditures, this dashboard allows us to monitor public spending by the +categories most critical in supporting an inclusive recovery from the COVID-19 +pandemic. + ```{r} + + data %>% mutate(topic = str_replace_all(topic, "; NA", "")) %>% select(program, program_description, geography, legislation, topic, building_blocks, racf_categories, allocation, spent, agency, source, source_url) %>% @@ -765,10 +877,12 @@ data %>%
    -For more information about the dashboard, please contact [Meg Norris](mailto:mnorris@racf.org) (The Rochester Area community Foundation) or [Christina Stacy](mailto:cstacy@urban.org) (Urban Institute). +For more information about the dashboard, please contact [Meg +Norris](mailto:mnorris@racf.org) (The Rochester Area community Foundation) or +[Christina Stacy](mailto:cstacy@urban.org) (Urban Institute). Click For [Glossary of Terms](glossary.qmd) -The code used to create this dashboard was writen by [Manuel Alcalá Kovalski](mailto:malcalakovalski@urban.org) and can be found on +The code used to create this dashboard was writen by [Manuel Alcalá +Kovalski](mailto:malcalakovalski@urban.org) and can be found on [GitHub](https://github.com/UI-Research/rochester-dashboard). -

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