The travel time data on this map is modeled from a 2005 transit network. The home values are as of 2000 and are expressed in year 2000 dollars. The home value estimates were created by the Association of Bay Area Governements by combining ParcelQuest real estate transaction data and real estate tax assessment data. This information can be generated for any address in the region using an interactive mapping tool available under Maps at onebayarea.org/maps.htm (Note - this tool is no longer available).

The California Tax Credit Allocation Committee and the California Department of Housing and Community Development have created an opportunity map to identify areas across California whose have characteristics to support long-term positive economic, education, and health outcomes for low-income families. These maps were created with the goal to increase access to high opportunity areas for families with children in housing financed with 9% Low Income Housing Tax Credits.Preprocessing methods: Kept all categories to display on the web map.

The California Tax Credit Allocation Committee and the California Department of Housing and Community Development have created an opportunity map to identify areas across California whose have characteristics to support long-term positive economic, education, and health outcomes for low-income families. These maps were created with the goal to increase access to high opportunity areas for families with children in housing financed with 9% Low Income Housing Tax Credits.Filtered for opportunity category (field name "oppcat") equal to "High Segregation & Poverty" or "Low Resource." Dissolved into one feature.

West Virginia and Kansas had the lowest cost of living across all U.S. states, with composite costs being half of those found in Hawaii. This was according to a composite index that compares prices for various goods and services on a state-by-state basis. In West Virginia, the cost of living index amounted to **** — well below the national benchmark of 100. Virginia— which had an index value of ***** — was only slightly above that benchmark. Expensive places to live included Hawaii, Massachusetts, and California. Housing costs in the U.S. Housing is usually the highest expense in a household’s budget. In 2023, the average house sold for approximately ******* U.S. dollars, but house prices in the Northeast and West regions were significantly higher. Conversely, the South had some of the least expensive housing. In West Virginia, Mississippi, and Louisiana, the median price of the typical single-family home was less than ******* U.S. dollars. That makes living expenses in these states significantly lower than in states such as Hawaii and California, where housing is much pricier. What other expenses affect the cost of living? Utility costs such as electricity, natural gas, water, and internet also influence the cost of living. In Alaska, Hawaii, and Connecticut, the average monthly utility cost exceeded *** U.S. dollars. That was because of the significantly higher prices for electricity and natural gas in these states.

SummaryThe Essential Connectivity Map shows a statewide network of 850 relatively intact Natural Landscape Blocks (ranging in size from 2,000 to about 3.7 million acres) connected by 192 Essential Connectivity Areas (Table 3.1). There are fewer Essential Connectivity Areas than Natural Landscape Blocks, because each Essential Connectivity Area serves to connect at least two, and as many as 15 Natural Landscape Blocks. Due to the broad, statewide nature of this map, and its focus on connecting very large blocks of mostly protected natural lands, the network omits many areas that are important to biological conservation. The purpose of the map is to focus attention on large areas important to maintaining ecological integrity at the broadest scale. Natural areas excluded from this broad-brush Essential Connectivity Network can therefore not be "written off" as unimportant to connectivity conservation or to sustaining California's natural heritage.DescriptionThe California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of California's diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be "written off" as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.CEHC Least Cost Corridors (LACo)Mosaic of least-cost corridor results for all Essential Connectivity Areas and clipped to the LA County Boundary. The minimum cell value was used for overlapping cells.CEHC Cost Surface (LACo)Statewide resistance surface generated for least-cost corridor models and clipped to the LA County Boundary.

This layer shows census tracts that meet the following definitions: Census tracts with median household incomes at or below 80 percent of the statewide median income or with median household incomes at or below the threshold designated as low income by the Department of Housing and Community Development’s list of state income limits adopted under Healthy and Safety Code section 50093 and/or Census tracts receiving the highest 25 percent of overall scores in CalEnviroScreen 4.0 or Census tracts lacking overall scores in CalEnviroScreen 4.0 due to data gaps, but receiving the highest 5 percent of CalEnviroScreen 4.0 cumulative population burden scores or Census tracts identified in the 2017 DAC designation as disadvantaged, regardless of their scores in CalEnviroScreen 4.0 or Lands under the control of federally recognized Tribes.

The City of Long Beach’s Site Inventory identifies a list of sites that are suitable for future residential development. California state law mandates that each jurisdiction ensure availability of an adequate number of sites that have appropriate zoning, development standards, and infrastructure capacity to meet its fair share of the regional housing need at all income levels. The inventory is a tool that maps out suitable sites for new housing development at different income affordability levels in order to meet the City’s Regional Housing Needs Assessment (RHNA) that is allocated by the state. Appendix C of the Housing Element provide additional information on the Long Beach Site Inventory and methodology used to identify suitable sites. For more information on site inventories and regulatory requirements, visit the California Housing and Community Development Department’s website.

Affordable Housing is point data representing social housing locations in Durham Region.

LAHD financed projects since 2003 to present. These projects are financed with programs including Affordable Housing Managed Pipeline, Supportive Housing Program, Affordable Housing Bond Program, and the Proposition HHH Supportive Housing Loan Program. This project list contains participants, property, units, construction and milestone information. Each line contains both site and project level information. Site level information are presented with "SITE_" in the column headers. Column headers without "SITE_" are project level information.

The California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of California's diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be "written off" as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

50 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2050.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents. Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley. How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life? Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

Important Note:The metadata description below mentions the Regional Housing Needs Assessment (or RHNA). Part of meeting RHNA Eligibility is satisfying a list of criteria set by the State of California that needs to be met in order to qualify. This dataset contains both RHNA Eligible and non-RHNA Eligible sites. Non-RHNA Eligible sites are those that didn't quite meet the eligibility criteria set by the state, but will be still eligible for Rezoning per Department of Regional Planning guidelines, and thus represents a full picture of ALL sites that are eligible for Rezoning. The official Housing Element Rezoning layer that was certified by the State of California is located here, but it should be noted that this layer only contains sites that are RHNA Eligible.IntroductionThis metadata is broken up into different sections that provide both a high-level summary of the Housing Element and more detailed information about the data itself with links to other resources. The following is an excerpt from the Executive Summary from the Housing Element 2021 – 2029 document:The County of Los Angeles is required to ensure the availability of residential sites, at adequate densities and appropriate development standards, in the unincorporated Los Angeles County to accommodate its share of the regional housing need--also known as the Regional Housing Needs Allocation (RHNA). Unincorporated Los Angeles County has been assigned a RHNA of 90,052 units for the 2021-2029 Housing Element planning period, which is subdivided by level of affordability as follows:Extremely Low / Very Low (<50% AMI) - 25,648Lower (50 - 80% AMI) - 13,691Moderate (80 - 120% AMI) - 14,180Above Moderate (>120% AMI) - 36,533Total - 90,052NOTES - Pursuant to State law, the projected need of extremely low income households can be estimated at 50% of the very low income RHNA. Therefore, the County’s projected extremely low income can be estimated at 12,824 units. However, for the purpose of identifying adequate sites for RHNA, no separate accounting of sites for extremely low income households is required. AMI = Area Median IncomeDescriptionThe Sites Inventory (Appendix A) is comprised of vacant and underutilized sites within unincorporated Los Angeles County that are zoned at appropriate densities and development standards to facilitate housing development. The Sites Inventory was developed specifically for the County of Los Angeles, and has built-in features that filter sites based on specific criteria, including access to transit, protection from environmental hazards, and other criteria unique to unincorporated Los Angeles County. Other strategies used within the Sites Inventory analysis to accommodate the County’s assigned RHNA of 90,052 units include projected growth of ADUs, specific plan capacity, selected entitled projects, and capacity or planned development on County-owned sites within cities. This accounts for approximately 38 percent of the RHNA. The remaining 62 percent of the RHNA is accommodated by sites to be rezoned to accommodate higher density housing development (Appendix B).Caveats:This data is a snapshot in time, generally from the year 2021. It contains information about parcels, zoning and land use policy that may be outdated. The Department of Regional Planning will be keeping an internal tally of sites that get developed or rezoned to meet our RHNA goals, and we may, in the future, develop some public facing web applications or dashboards to show the progress. There may even be periodic updates to this GIS dataset as well, throughout this 8-year planning cycle.Update History:12/18/24 - Following the completion of the annexation to the City of Whittier on 11/12/24, 27 parcels were removed along Whittier Blvd which contained 315 Very Low Income units and 590 Above Moderate units. Following a joint County-City resolution of the RHNA transfer to the city, 247 Very Low Income units and 503 Above Moderate units were taken on by Whittier. 10/23/24 - Modifications were made to this layer during the updates to the South Bay and Westside Area Plans following outreach in these communities. In the Westside Planning area, 29 parcels were removed and no change in zoning / land use policy was proposed; 9 Mixed Use sites were added. In the South Bay, 23 sites were removed as they no longer count towards the RHNA, but still partially changing to Mixed Use.5/31/22 – Los Angeles County Board of Supervisors adopted the Housing Element on 5/17/22, and it received final certification from the State of California Department of Housing and Community Development (HCD) on 5/27/22. Data layer published on 5/31/22.Links to other resources:Department of Regional Planning Housing Page - Contains Housing Element and it's AppendicesHousing Element Update - Rezoning Program Story Map (English, and Spanish)Southern California Association of Governments (SCAG) - Regional Housing Needs AssessmentCalifornia Department of Housing and Community Development Housing Element pageField Descriptions:OBJECTID - Internal GIS IDAIN - Assessor Identification Number*SitusAddress - Site Address (Street and Number) from Assessor Data*Use Code - Existing Land Use Code (corresponds to Use Type and Use Description) from Assessor Data*Use Type - Existing Land Use Type from Assessor Data*Use Description - Existing Land Use Description from Assessor Data*Vacant / Nonvacant – Parcels that are vacant or non-vacant per the Use Code from the Assessor Data*Units Total - Total Existing Units from Assessor Data*Max Year - Maximum Year Built from Assessor Data*Supervisorial District (2021) - LA County Board of Supervisor DistrictSubmarket Area - Inclusionary Housing Submarket AreaPlanning Area - Planning Areas from the LA County Department of Regional Planning General Plan 2035Community Name - Unincorporated Community NamePlan Name - Land Use Plan Name from the LA County Department of Regional Planning (General Plan and Area / Community Plans)LUP - 1 - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 1 (% area) - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 2 - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 2 (% area) - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 3 - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 3 (% area) - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*Current LUP (Description) – This is a brief description of the land use category. In the case of multiple land uses, this would be the land use category that covers the majority of the parcel*Current LUP (Min Density - net or gross) - Minimum density for this category (as net or gross) per the Land Use Plan for this areaCurrent LUP (Max Density - net or gross) - Maximum density for this category (as net or gross) per the Land Use Plan for this areaProposed LUP – Final – The proposed land use category to increase density.Proposed LUP (Description) – Brief description of the proposed land use policy.Prop. LUP – Final (Min Density) – Minimum density for the proposed land use category.Prop. LUP – Final (Max Density) – Maximum density for the proposed land use category.Zoning - 1 - Zoning from Dept. of Regional Planning - Primary Zone (in cases where there are more than one zone category present)*Zoning - 1 (% area) - Zoning from Dept. of Regional Planning - Primary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 2 - Zoning from Dept. of Regional Planning - Secondary Zone (in cases where there are more than one zone category present)*Zoning - 2 (% area) - Zoning from Dept. of Regional Planning - Secondary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 3 - Zoning from Dept. of Regional Planning - Tertiary Zone (in cases where there are more than one zone category present)*Zoning - 3 (% area) - Zoning from Dept. of Regional Planning - Tertiary Zone (% of parcel covered in cases where there are more than one zone category present)*Current Zoning (Description) - This is a brief description of the zoning category. In the case of multiple zoning categories, this would be the zoning that covers the majority of the parcel*Proposed Zoning – Final – The proposed zoning category to increase density.Proposed Zoning (Description) – Brief description of the proposed zoning.Acres - Acreage of parcelMax Units Allowed - Total Proposed Land Use Policy UnitsRHNA Eligible? – Indicates whether the site is RHNA Eligible or not. Very Low Income Capacity - Total capacity for the Very Low Income level as defined in the Housing ElementLow Income Capacity - Total capacity for the Low Income level as defined in the Housing ElementModerate Income Capacity - Total capacity for the Moderate Income level as defined in the Housing ElementAbove Moderate Income Capacity - Total capacity for the Above Moderate Income level as defined in the Housing ElementRealistic Capacity - Total Realistic Capacity of parcel (totaling all income levels). Several factors went into this final calculation. See the Housing Element (Links to Other Resources above) in the following locations - "Sites Inventory - Lower Income RHNA" (p. 223), and "Rezoning - Very Low / Low Income RHNA" (p231).Income Categories - Income Categories assigned to the parcel (relates

Assisted Living Waiver (ALW) eligible individuals are those who are enrolled in Medi-Cal and meet the level of care provided in a nursing facility due to their medical needs. Individuals with Medi-Cal benefits that include a share of cost may not enroll in the ALW. This dataset contains the provider number, provider legal name, provider business name, capacity per provider enrollment, provider physical location, provider counties and provider phone number of facilities enrolled in the ALW program. Data as of 1/1/2023

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

Local, state, tribal, and federal agencies use disability data to plan and fund programs for people with disabilities. Disability data helps communities enroll eligible households in programs designed to assist them such as health care programs and affordable housing programs. Disability data also helps local jurisdictions provide services that:Enable older adults to remain living safely in their homes and communities (Older Americans Act).Provide services and assistance to people with a disability, such as financial assistance with utilities (Low Income Home Energy Assistance Program)Disability data helps communities qualify for grants such as the Community Development Block Grant (CDBG) Program, the HOME Investment Partnership Program, the Emergency Solutions Grants (ESG) Program, the Housing Opportunities for Persons with AIDS (HOPWA) Program, and other local and federal programs.Disability data are also used to evaluate other government programs and policies to ensure that they fairly and equitably serve the needs of all groups, as well as enforce laws, regulations, and policies against discrimination.This map shows the count and prevalence of people with a disability. This includes people with a hearing difficulty, a vision difficulty, an ambulatory difficulty, a cognitive difficulty, a self-care difficulty, and an independent-living difficulty. The features in web map are symbolized using color and size to depict total population with a disability count (size of symbol) and prevalence (color of symbol). Web map is multi-scaled, and opens displaying data for counties and tracts. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

The “COG Geography TCAC/HCD Opportunity Map” uses the same methodology as the TCAC/HCD Opportunity Map but incorporates the following changes:The reference geography is changed from the California Tax Credit Allocation Committee (TCAC) region to the Council of Governments (COG).All tracts within COGs are scored against each other. Tracts that do not fall within a COG are scored against tracts within their county.All areas are assessed at the tract level.For more on the TCAC/HCD Opportunity Map methodology, click here: https://www.treasurer.ca.gov/ctcac/opportunity.asp Similar to the TCAC/HCD Opportunity Map, this layer seeks to identify areas in every region of the state whose characteristics have been shown by research to support positive economic, educational, and health outcomes for low-income families—particularly long-term outcomes for children. The methodology changes described above are intended to better align the map with RHNA and Housing Element goals and geographies.For example, this map scores areas against all other areas within the same COG—or the same county, for non-COG areas—rather than against TCAC-defined regions, which are different than COGs. The TCAC/HCD Opportunity Map uses TCAC regions as the reference geography because the map was specifically designed for application in the 9% LIHTC program, which ranks affordable housing funding projects against other projects in the same TCAC-defined region.In addition, this map does not designate nor adopt a distinct approach to scoring rural areas, but instead scores all areas in the same region on the same scale and at the tract level. This approach contrasts with the TCAC/HCD Opportunity Map, which scores rural areas separately—because rural affordable housing developments compete in a separate funding pool.

The travel time map was generated using the Pedestrian Evacuation Analyst model from the USGS. The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped into 1-minute increments for easier visualization. The times in the attribute table represent the estimated time to travel on foot to the nearest safe zone at the speed designated in the map title. The bridge or nobridge name in the map title identifies whether bridges were represented in the modeling or whether they were removed prior to modeling to estimate the impact on travel times from earthquake-damaged bridges.

The travel time map was generated using the Pedestrian Evacuation Analyst model from the USGS. The travel time analysis uses ESRI's Path Distance tool to find the shortest distance across a cost surface from any point in the hazard zone to a safe zone. This cost analysis considers the direction of movement and assigns a higher cost to steeper slopes, based on a table contained within the model. The analysis also adds in the energy costs of crossing different types of land cover, assuming that less energy is expended walking along a road than walking across a sandy beach. To produce the time map, the evacuation surface output from the model is grouped into 1-minute increments for easier visualization. The times in the attribute table represent the estimated time to travel on foot to the nearest safe zone at the speed designated in the map title. The bridge or nobridge name in the map title identifies whether bridges were represented in the modeling or whether they were removed prior to modeling to estimate the impact on travel times from earthquake-damaged bridges.

In 2017, the California Tax Credit Allocation Committee (CTCAC) and the Department of Housing and Community Development (HCD) created the California Fair Housing Task Force (Task Force). The Task Force was asked to assist CTCAC and HCD in creating evidence-based approaches to increasing access to opportunity for families with children living in housing subsidized by the Low-Income Housing Tax Credit (LIHTC) program.

This feature set contains Resource Opportunity Areas (ROAs) that are the results of the Task Force's analysis for the two regions used for the San Francisco Bay Region; one is for the cities and towns (urban) and the other is for the rural areas. The reason for treating urban and rural areas as separate reasons is that using absolute thresholds for place-based opportunity could introduce comparisons between very different areas of the total region that make little sense from a policy perspective — in effect, holding a farming community to the same standard as a dense, urbanized neighborhood.

ROA analysis for urban areas is based on census tract data. Since tracts in rural areas of are approximately 37 times larger in land area than tracts in non-rural areas, tract-level data in rural areas may mask over variation in opportunity and resources within these tracts. Assessing opportunity at the census block group level in rural areas reduces this difference by 90 percent (each rural tract contains three block groups), and thus allows for finer-grained analysis.

In addition, more consistent standards can be useful for identifying areas of concern from a fair housing perspective — such as high-poverty and racially segregated areas. Assessing these factors based on intraregional comparison could mischaracterize areas in more affluent areas with relatively even and equitable development opportunity patterns as high-poverty, and could generate misleading results in areas with higher shares of objectively poor neighborhoods by holding them to a lower, intraregional standard.

To avoid either outcome, the Task Force used a hybrid approach for the CTCAC/HCD ROA analysis — accounting for regional differences in assessing opportunity for most places, while applying more rigid standards for high-poverty, racially segregated areas in all regions. In particular:

Filtering for High-Poverty, Racially Segregated Areas The CTCAC/HCD ROA filters areas that meet consistent standards for both poverty (30% of the population below the federal poverty line) and racial segregation (over-representation of people of color relative to the county) into a “High Segregation & Poverty” category. The share of each region that falls into the High Segregation & Poverty category varies from region to region.

Calculating Index Scores for Non-Filtered Areas The CTCAC/HCD ROAs process calculates regionally derived opportunity index scores for non-filtered tracts and rural block groups using twenty-one indicators (see Data Quality section of metadata for more information). These index scores make it possible to sort each non-filtered tract or rural block group into opportunity categories according to their rank within the urban or rural areas.

To allow CTCAC and HCD to incentivize equitable development patterns in each region to the same degree, the CTCAC/HCD analysis 20 percent of tracts or rural block groups in each urban or rural area, respectively, with the highest relative index scores to the "Highest Resource” designation and the next 20 percent to the “High Resource” designation.

The region's urban area thus ends up with 40 percent of its total tracts with reliable data as Highest or High Resource (or 40 percent of block groups in the rural area). The remaining non-filtered tracts or rural block groups are then evenly divided into “Low Resource” and “Moderate Resource” categories.

Excluding Tracts or Block Groups The analysis also excludes certain census areas from being categorized. To improve the accuracy of the mapping, tracts and rural block groups with the following characteristics are excluded from the application of the filter and from categorization based on index scores: ● Areas with unreliable data, as defined later in this document; ● Areas where prisoners make up at least 75 percent of the population; ● Areas with population density below 15 people per square mile and total population below 500; and ● Areas where at least half of the age 16+ population is employed by the armed forces, in order to exclude military base areas where it is not possible to develop non-military affordable housing.

Excluded tracts and rural block groups are identified as “nan” in the attribute table.

The full methodology used by the Task Force can be found in the California Fair Housing Task Force Opportunity Mapping Methodology report (https://www.treasurer.ca.gov/ctcac/opportunity/2022/2022-hcd-methodology.pdf) on the California Office of State Treasurer website.

Source data and maps can be found on the CTCAC/HCD Opportunity Area Maps page (https://www.treasurer.ca.gov/ctcac/opportunity.asp).