diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json index 83fac45..e033b18 100644 --- a/_freeze/index/execute-results/html.json +++ b/_freeze/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "75626011abc405fdf078e9e383e63c45", + "hash": "2ccf9e7440f1ff7bb5a2e86439a0e704", "result": { "engine": "knitr", - "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\"\neditor: \n markdown: \n wrap: 80\n\n\n---\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n# WIP DO NOT SHARE\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
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Percent of funds Allocated towards an Inclusive Recovery in the Region

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65%

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Percent of funds Allocated towards an Inclusive Recovery in the City

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55%

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Percent of funds allocated toward an Inclusive Recovery in the County:

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85%

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\n```\n\n:::\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, but to increase equity and remediate some of the past\ninequities that have been [built into our nation’s laws and\nprograms](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).\nTo 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 [State and Local\nFiscal Recovery Funds\n(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)\nprovided through the American Rescue Plan Act (ARPA). SLFRF must be obligated by\nthe end of 2024 and spent by the end of 2026. The dashboard focuses on SLFRF\nbecause of their flexibility in giving local governments authority to decide how\nthey should be spent.\n\n**The Building Blocks of An Inclusive Recovery**\n![](images/building-blocks.png){fig-cap-location=\"top\"}\n\nThe dashboard is laid out as follows:\n\n- Alignment of allocations with the building blocks of an inclusive recovery,\n shown above\n- How funds are being spent by policy area and subtopic\n- Alignment of allocations with Rochester Area Community Foundation’s\n priorities \n- Mapping of the City of Rochester’s SLFRF capital investments onto original\n redlining maps for the city and current racial characteristics of\n neighborhoods\n- Detailed table of all allocations and their descriptions\n\n::: {.callout-note}\n**This dashboard does not track outcomes from these investments – it only\nshows where the dollars are flowing.** Simply allocating funds toward topics that could increase equity and inclusion\ndoes not guarantee that they do so. Future research efforts should closely\nmonitor outcomes and impacts from the recovery dollar investments to ensure that\nthey close equity gaps exacerbated by the pandemic and address the root causes\nof inequities.\n:::\n\n\n\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\nBy our estimation, **65%** of city and county 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\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-6-1.png){width=672}\n:::\n:::\n\n\nOut of the 65% of funds allocated to programs that align with the five building\nblocks of an inclusive recovery, the most funding has been allocated to\nreinvesting in disinvested communities while the least has been allocated to\ncreating jobs for residents hardest hit by the pandemic or who face the greatest\nbarriers to employment. The figure below shows how that funding is divided among\nthe five building blocks and includes only the funds allocated to programs that\nalign with the building blocks (the 65%).\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-7-1.png){width=672}\n:::\n:::\n\n\n## What policy areas are being funded?\n\nThe figure below shows how funding has been allocated across policy categories\nand includes all allocations, not just those that align with the building blocks\nof an inclusive recovery.\n\nBy category, most of the funds have been allocated to Community and Economic\nDevelopment, Infrastructure, and Housing, while the least have been allocated to\nOperations, Public Health and COVID-19 Response, and Social Services.\n\n\n::: {.cell}\n\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-9-1.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-10-1.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-11-1.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-12-1.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-13-1.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-14-1.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-15-1.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-16-1.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-17-1.png){width=672}\n:::\n:::\n\n:::\n\n## How much has been spent?\n\nThe City of Rochester and Monroe County have allocated almost $301M in SLFRF\nfunds, and, of those, 14.6% have been reported as spent.\n\n::: {.callout-note}\n\nNo data is available for Monroe County\n\n:::\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-18-1.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, **61.1%** of SLFRF funding has been allocated towards these priority areas.\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/fig-pct-allocated-towards-racf-priorities-1.png){#fig-pct-allocated-towards-racf-priorities width=672}\n:::\n:::\n\n\nThe figure below shows how SLFRF funding is divided among the priority topics\nand includes only the funds allocated to programs that align with one of RACF’s\npriority areas. The majority of the aligned funds are for partnering against\npoverty, followed by advancing environmental justice. Less well funded through\nSLFRF are preserving historical assets, promoting successful aging, and\nsupporting arts and culture.\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-21-1.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/Latino1 in a neighborhood.**.\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 (areas most desirable for lending\npurposes), blue (still desirable), yellow (declining), and red (the riskiest for\nmortgage support). These grades were largely based on the neighborhood’s racial,\nethnic, socioeconomic, and religious composition. Generally, White, middle-class\nneighborhoods received FHA home loans, whereas many Black and Hispanic/Latino\nneighborhoods were deemed hazardous and declining in value and did not receive\nFHA insured mortgages or loans. 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\n**To increase equity in the region, leaders must focus investments in\ncommunities that have been underinvested in historically.** These investments\nmust be those that , meaning that they should not be investments that only\nbenefit new, incoming wealthier residents, or those that have negative impacts\non immediate neighbors, like a sewage treatment facility or a highway that\npollutes the air. And, they must be investments that , particularly historically\nexcluded members. The maps below give us a first approximation of how equitably\ninvestments are distributed. We encourage further exploration of how these\ninvestments align with the goals of community members and how much they benefit\nhistorically excluded residents.\n\n::: panel-tabset\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n### Redlining\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\n### Percent Black\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n### Percent Hispanic/Latino\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
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\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\n**Note:** 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\n```{=html}\n
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\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\n```{=html}\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", + "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\"\neditor: \n markdown: \n wrap: 80\n\n\n---\n\n::: {.cell layout-align=\"center\"}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n# Work In Progress: DO NOT SHARE\n\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
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Percent of funds Allocated towards an Inclusive Recovery in the Region

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Percent of funds Allocated towards an Inclusive Recovery in the City

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Percent of funds allocated toward an Inclusive Recovery in the County:

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\n```\n\n:::\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, but to increase equity and remediate some of the past\ninequities that have been [built into our nation’s laws and\nprograms](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).\nTo 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 [State and Local\nFiscal Recovery Funds\n(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)\nprovided through the American Rescue Plan Act (ARPA). SLFRF must be obligated by\nthe end of 2024 and spent by the end of 2026. The dashboard focuses on SLFRF\nbecause of their flexibility in giving local governments authority to decide how\nthey should be spent.\n\n**The Building Blocks of An Inclusive Recovery**\n![](images/building-blocks.png){fig-cap-location=\"top\"}\n\nThe dashboard is laid out as follows:\n\n- Alignment of allocations with the building blocks of an inclusive recovery,\n shown above\n- How funds are being spent by policy area and subtopic\n- Alignment of allocations with Rochester Area Community Foundation’s\n priorities \n- Mapping of the City of Rochester’s SLFRF capital investments onto original\n redlining maps for the city and current racial characteristics of\n neighborhoods\n- Detailed table of all allocations and their descriptions\n\n**This dashboard does not track outcomes from these investments – it only\nshows where the dollars are flowing.** Simply allocating funds toward topics that could increase equity and inclusion\ndoes not guarantee that they do so. Future research efforts should closely\nmonitor outcomes and impacts from the recovery dollar investments to ensure that\nthey close equity gaps exacerbated by the pandemic and address the root causes\nof inequities.\n\n\n\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\nBy our estimation, **65%** of city and county 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\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-6-1.png){width=672}\n:::\n:::\n\n\nOut of the 65% of funds allocated to programs that align with the five building\nblocks of an inclusive recovery, the most funding has been allocated to\nreinvesting in disinvested communities while the least has been allocated to\ncreating jobs for residents hardest hit by the pandemic or who face the greatest\nbarriers to employment. The figure below shows how that funding is divided among\nthe five building blocks and includes only the funds allocated to programs that\nalign with the building blocks (the 65%).\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-7-1.png){width=672}\n:::\n:::\n\n\n## What policy areas are being funded?\n\nThe figure below shows how funding has been allocated across policy categories\nand includes all allocations, not just those that align with the building blocks\nof an inclusive recovery.\n\nBy category, most of the funds have been allocated to Community and Economic\nDevelopment, Infrastructure, and Housing, while the least have been allocated to\nOperations, Public Health and COVID-19 Response, and Social Services.\n\n\n::: {.cell}\n\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-9-1.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-10-1.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-11-1.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-12-1.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-13-1.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-14-1.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-15-1.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-16-1.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-17-1.png){width=672}\n:::\n:::\n\n:::\n\n## How much has been spent?\n\nThe City of Rochester and Monroe County have allocated almost $301M in SLFRF\nfunds, and, of those, 14.6% have been reported as spent.\n\n\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-18-1.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\n\n- Preserving historic assets\n\n- Advancing environmental justice and sustainability\n\n- Promoting successful aging\n\n\n::: {.cell}\n\nOverall, **61.1%** of SLFRF funding has been allocated towards these priority areas.\n:::\n\n::: {.cell}\n::: {.cell-output-display}\n![](index_files/figure-html/unnamed-chunk-20-1.png){width=672}\n:::\n:::\n\n\nThe figure below shows how SLFRF funding is divided among the priority topics\nand includes only the funds allocated to programs that align with one of RACF’s\npriority areas. The majority of the aligned funds are for partnering against\npoverty, followed by advancing environmental justice. Less well funded through\nSLFRF are preserving historical assets, promoting successful aging, and\nsupporting arts and culture.\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-21-1.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 in a neighborhood.**\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 (areas most desirable for lending\npurposes), blue (still desirable), yellow (declining), and red (the riskiest for\nmortgage support). These grades were largely based on the neighborhood’s racial,\nethnic, socioeconomic, and religious composition. Generally, White, middle-class\nneighborhoods received FHA home loans, whereas many Black and Hispanic/Latino\nneighborhoods were deemed hazardous and declining in value and did not receive\nFHA insured mortgages or loans. 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\n**To increase equity in the region, leaders must focus investments in\ncommunities that have been underinvested in historically.** These investments\nmust be those that , meaning that they should not be investments that only\nbenefit new, incoming wealthier residents, or those that have negative impacts\non immediate neighbors, like a sewage treatment facility or a highway that\npollutes the air. And, they must be investments that , particularly historically\nexcluded members. The maps below give us a first approximation of how equitably\ninvestments are distributed. We encourage further exploration of how these\ninvestments align with the goals of community members and how much they benefit\nhistorically excluded residents.\n\n::: panel-tabset\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n:::\n\n\n### Redlining\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n::: {.cell}\n\n:::\n\n\n### Percent Black\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\n\n```\n\n:::\n:::\n\n\n### Percent Hispanic/Latino\n\n\n::: {.cell}\n::: {.cell-output-display}\n\n```{=html}\n
\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\n**Note:** 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\n```{=html}\n
\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 ACT Rochester and [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\n```{=html}\n\n```\n\n:::\n:::\n\n\n
\n\nFor more information about the dashboard, please contact [Meg\nNorris](mailto:mnorris@racf.org) (ACT Rochester) 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" ], diff --git a/_freeze/index/figure-html/fig-pct-allocated-towards-racf-priorities-1.png b/_freeze/index/figure-html/fig-pct-allocated-towards-racf-priorities-1.png index 5485503..1f5ba7a 100644 Binary files a/_freeze/index/figure-html/fig-pct-allocated-towards-racf-priorities-1.png and b/_freeze/index/figure-html/fig-pct-allocated-towards-racf-priorities-1.png differ diff --git a/_freeze/index/figure-html/unnamed-chunk-10-1.png b/_freeze/index/figure-html/unnamed-chunk-10-1.png index cc9745d..a754f25 100644 Binary files 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diff --git a/images/building-blocks.png b/images/building-blocks.png index 7a95162..ebc0cc1 100644 Binary files a/images/building-blocks.png and b/images/building-blocks.png differ diff --git a/index.qmd b/index.qmd index 0d21d19..98c9a69 100644 --- a/index.qmd +++ b/index.qmd @@ -60,10 +60,13 @@ librarian::shelf( options(tigris_use_cache = TRUE) set_urbn_defaults(style = "print", base_size = 12) box_auth() -racf_palette <- c("#D6A123", "#ED0A72", "#D442CF", "#696B4F", "#8FAEBE") +racf_palette <- c( + "#D6A123", "#ED0A72", "#D442CF", "#696B4F", "#8FAEBE", + "#AB5833", "#978638", "#5D4E78", "#36689F", "#70910B" +) ``` -# WIP DO NOT SHARE +# Work In Progress: DO NOT SHARE ```{r load-data} data_url <- "https://urbanorg.box.com/shared/static/a62wrsemjiaezr43c3lnkqlf2t69ymlv.xlsx" @@ -129,21 +132,21 @@ library(bsicons) vbs <- list( value_box( title = "Percent of funds Allocated towards an Inclusive Recovery in the Region", - value = "65%", - showcase = bs_icon("bar-chart"), - theme = "purple", + value = htmltools::tags$strong("65%"), + theme = value_box_theme(bg = "#fff1cc", fg = "#AB5833"), + fill = FALSE, ), value_box( title = "Percent of funds Allocated towards an Inclusive Recovery in the City", - value = "55%", - showcase = bs_icon("graph-up"), - theme = "teal", + value = htmltools::tags$strong("55%"), + theme = value_box_theme(bg = "#ffffd1", fg = "#978638"), + fill = FALSE, ), value_box( title = "Percent of funds allocated toward an Inclusive Recovery in the County: ", - value = "85%", - showcase = bs_icon("pie-chart"), - theme = "pink", + value = htmltools::tags$strong("85%"), + theme = value_box_theme(bg = "#e9daff", fg = "#5D4E78"), + fill = FALSE, ) ) @@ -192,14 +195,12 @@ The dashboard is laid out as follows: neighborhoods - Detailed table of all allocations and their descriptions -::: {.callout-note} **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. -::: @@ -255,17 +256,25 @@ pct_allocated_inclusive_recovery <- ) |> mutate(geography = factor(geography, levels = c("Monroe County", "Rochester", "Overall") + )) |> + mutate(building_block = factor(building_block, + levels = rev(c("Building Block", "Other")) )) pct_allocated_inclusive_recovery |> ggplot(aes(fill = building_block, y = percentage, x = geography)) + - geom_bar(position = "fill", stat = "identity") + + geom_bar(position = "fill", stat = "identity", width = 0.5) + scale_y_continuous(labels = scales::percent_format()) + + scale_fill_manual( + values = c(racf_palette[7], racf_palette[8]), + guide = guide_legend(reverse = TRUE) + ) + coord_flip() + labs( x = NULL, y = NULL, - title = "Percent Allocated Toward an Inclusive Recovery" - ) + title = NULL + ) + + guides(color = guide_legend(reverse = FALSE)) ``` Out of the 65% of funds allocated to programs that align with the five building @@ -297,7 +306,10 @@ data_reordered <- summarise(allocation = sum(allocation, na.rm = TRUE), .by = c("building_blocks", "geography")) %>% drop_na() %>% mutate(total_allocation = sum(allocation), .by = "building_blocks") %>% - mutate(building_blocks = reorder(building_blocks, total_allocation)) + mutate(building_blocks = reorder(building_blocks, total_allocation)) |> + mutate(geography = factor(geography, + levels = c("Monroe County", "Rochester") + )) data_reordered %>% ggplot(aes(x = building_blocks, y = allocation, fill = geography)) + @@ -308,9 +320,11 @@ data_reordered %>% breaks = seq(0, 100 * 1e6, by = 25 * 1e6) ) + scale_x_discrete(labels = function(x) str_wrap(x, width = 20)) + - scale_fill_discrete(guide = guide_legend(reverse = TRUE)) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + labs(x = NULL, y = "Total Allocation") ``` @@ -368,10 +382,12 @@ ggplot(data_reordered, aes(x = topic, y = allocation, fill = geography)) + limits = c(0, 100 * 1e6), breaks = seq(0, 100 * 1e6, 25 * 1e6) ) + - scale_fill_discrete(guide = guide_legend(reverse = TRUE)) + coord_flip() + labs(x = NULL, y = "Total allocation") + - scale_fill_manual(values = racf_palette) + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) ``` ## Spent @@ -398,13 +414,12 @@ data %>% labels = scales::label_number(scale = 1 / 1e6, suffix = "M", prefix = "$"), limits = c(0, 100 * 1e6) ) + - scale_fill_manual(values = "#fdbf11") + + scale_fill_manual(values = racf_palette[2]) + coord_flip() + labs( x = NULL, y = "Total spent", caption = "Note: Data on allocations spent were only available from the city of Rochester." - ) + - scale_fill_manual(values = racf_palette) + ) ``` ::: @@ -430,7 +445,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -449,7 +467,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette[2], + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -468,7 +489,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -487,7 +511,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -506,7 +533,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -525,7 +555,10 @@ data %>% limits = c(0, 50 * 1e6) ) + coord_flip() + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + scale_x_discrete(limits = rev) + labs(x = NULL, y = "Total allocation") ``` @@ -545,7 +578,10 @@ data %>% ) + coord_flip() + scale_x_discrete(limits = rev) + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette[2], + guide = guide_legend(reverse = TRUE) + ) + labs(x = NULL, y = "Total allocation") ``` ::: @@ -555,36 +591,40 @@ data %>% The City of Rochester and Monroe County have allocated almost $301M in SLFRF funds, and, of those, 14.6% have been reported as spent. -::: {.callout-note} - -No data is available for Monroe County - -::: ```{r} pct_spent <- data |> summarise( spent = sum(spent, na.rm = TRUE), - .by = c("building_block", "geography") + allocation = sum(allocation, na.rm = TRUE), + .by = c("geography") ) |> mutate( - percentage = spent / sum(spent), + pct_spent = spent / sum(allocation), + pct_unspent = 1 - pct_spent, .by = c("geography") ) |> - mutate(geography = factor(geography, - levels = c("Monroe County", "Rochester", "Overall") - )) + pivot_longer( + cols = starts_with("pct"), + names_to = "spending_status", + values_to = "percentage" + ) |> + mutate(spending_status = str_remove(spending_status, "pct_") |> + str_to_title() |> factor(levels = c("Unspent", "Spent"))) pct_spent |> - mutate(percentage = if_else(is.na(percentage), NA_integer_, percentage)) |> - ggplot(aes(fill = building_block, y = percentage, x = geography)) + - geom_bar(position = "fill", stat = "identity", na.rm = FALSE) + + ggplot(aes(fill = spending_status, y = percentage, x = geography)) + + geom_bar(position = "fill", stat = "identity", na.rm = FALSE, width = 0.4) + scale_y_continuous(labels = scales::percent_format()) + + scale_fill_manual( + values = c(racf_palette[7], racf_palette[8]), + guide = guide_legend(reverse = TRUE) + ) + coord_flip() + labs( x = NULL, y = NULL, - title = "Percent Allocated Tward an Inclusive Recovery" + caption = "Note: No data is available for Monroe County" ) ``` @@ -607,17 +647,19 @@ allocated_to_racf_pct <- The Rochester Area Community Foundation's key priorities for investment in the region include: -- Closing the academic achievement and opportunity gap; +- Closing the academic achievement and opportunity gap + +- Fostering racial and ethnic understanding and equity -- Fostering racial and ethnic understanding and equity; +- Partnering against poverty -- Partnering against poverty; +- Supporting arts and culture -- Supporting arts and culture; Preserving historic assets; +- Preserving historic assets -- Advancing environmental justice and sustainability; and +- Advancing environmental justice and sustainability -- Promoting successful aging. +- Promoting successful aging ```{epoxy, .transformer = epoxy_transform_inline(.percent = scales::label_percent(accuracy = 0.1))} @@ -627,9 +669,6 @@ Overall, **{.pct allocated_to_racf_pct}** of SLFRF funding has been allocated to ``` ```{r} -#| label: fig-pct-allocated-towards-racf-priorities - - pct_allocated_racf_overall <- data |> mutate(racf_category = if_else(is.na(racf_categories), "Other", "RACF Priority")) |> @@ -651,18 +690,26 @@ pct_allocated_racf_recovery <- percentage = allocated / sum(allocated), .by = c("geography") ) |> - mutate(geography = factor(geography, - levels = c("Monroe County", "Rochester", "Overall") - )) + mutate( + geography = factor(geography, + levels = c("Monroe County", "Rochester", "Overall") + ), + racf_category = factor(racf_category, + levels = rev(c("RACF Priority", "Other")) + ) + ) pct_allocated_racf_recovery |> ggplot(aes(fill = racf_category, y = percentage, x = geography)) + - geom_bar(position = "fill", stat = "identity") + + geom_bar(position = "fill", stat = "identity", width = 0.5) + scale_y_continuous(labels = scales::percent_format()) + + scale_fill_manual( + values = c(racf_palette[7], racf_palette[8]), + guide = guide_legend(reverse = TRUE) + ) + coord_flip() + labs( - x = NULL, y = NULL, - title = "Percent Allocated Toward an Inclusive Recovery" + x = NULL, y = NULL ) ``` @@ -684,8 +731,10 @@ data %>% geom_col() + scale_y_continuous(labels = scales::label_number(scale = 1 / 1e6, suffix = "M", prefix = "$")) + scale_x_discrete(labels = function(x) str_wrap(x, width = 20)) + - scale_fill_discrete(guide = guide_legend(reverse = TRUE)) + - scale_fill_manual(values = racf_palette) + + scale_fill_manual( + values = racf_palette, + guide = guide_legend(reverse = TRUE) + ) + coord_flip() + labs(x = NULL, y = "Total Allocation") ``` @@ -696,7 +745,7 @@ 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/Latino1 in a neighborhood.**. +percent of residents who are Hispanic/Latino in a neighborhood.** [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 @@ -1035,7 +1084,7 @@ 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 +partnership with and support from ACT Rochester and [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 @@ -1060,7 +1109,7 @@ data %>%
For more information about the dashboard, please contact [Meg -Norris](mailto:mnorris@racf.org) (The Rochester Area community Foundation) or +Norris](mailto:mnorris@racf.org) (ACT Rochester) or [Christina Stacy](mailto:cstacy@urban.org) (Urban Institute). Click For [Glossary of Terms](glossary.qmd)