Background
Listed Grant Opportunities
Intro to the Agricultural Land Prioritization Tool
Agricultural Land Prioritization Tool User Guide
About the Data
Acknowledgements

This map is a product of a Sustainable Agricultural Lands Conservation Grant with the primary aim of identifying agricultural land that is in danger of being converted to be lost or land that has the potential to become new sites of agriculture-whether urban or rural.

The map was built to meet two of the primary objectives of this grant project:(1) to identify priority parcels of land to be conserved or developed into long-term agricultural use via acquisition or easement to reinforce urban growth boundaries and (2) to identify priority parcels of agricultural land or land that could be converted to agricultural use for new/future urban farms or community gardens within underserved communities. The final map is therefore meant to aid organizations and agencies in determining programmatic fit of specific parcels of land for certain grant programs while still being a useful tool for long-term agricultural conservation planning. The map can build highly customizable models that are based on the priorities of each user and/or a handful of grants that can facilitate agricultural land acquisition.

This map is meant to help local agencies, planners, land trusts, and others explore agricultural and environmental characteristics of Alameda County to identify areas of Alameda County that may be high priorities for different policy goals and funding priorities. The interactive maps are designed to have some Basic Information that can be turned on and off including city limits, sphere of influence, Association of Bay Area Governments' (ABAG) Growth Geographies, Williamson Act parcels, protected areas (e.g., parks), and parcels under conservation easements. Then there are four different models that can be customized based on four common grants that are used for acquiring agricultural land and developing conservation easements. Each model allows the user to prioritize each criteria on a 1 to 5 scale. The models, one for each grant, can highlight specific areas in Alameda County as priority agricultural areas. This can be helpful for planning purposes. Additionally, it is also possible for users to upload shapefiles of parcels to either map to see if a parcel falls into any of the priority areas for one of the four grants. Users can also compare grants for a given parcel to see which one would be most appropriate. See additional details about the map in the Intro to the Agicultural Land Prioritization Tool and Agriculutral Land Prioritization Tool User Guide sections.

Program Name	Description	Eligible Applicants	Web Link
Department of Conservation - Sustainable Agricultural Land Conservation Program	The program provides funding for agricultural land protection that prevents conversion to more greenhouse gas intensive uses. Funding is available for capacity building, acquisitions, and planning.	Eligible applicants depend on the program component. For the Land Acquisition grant type, the following entities are eligible: Cities Counties Non-profit organizations Resources Conservation Districts Regional park and open space districts or authorities Federally and non federally recognized California Native American Tribes	SALC Guidelines
Wildlife Conservation Board - Land Acquisition Program	The program provides funding for land acquisition that supports the following objectives: Protects biodiversity; Climate change resilience and connectivity; the State Wildlife Action Plan; Conserves or enhances working landscapes; Conserves or enhances water-related projects; and/or Enhances public access.	Non-profit organizations Local government agencies Federal agencies State agencies CA Native American Tribes	Land Acquisition Program
State Coastal Conservancy - Land Protection Program	The program provides funding for land protection projects that meet priorities listed in the 2023-2027 Strategic Plan. The strategic plan references statewide and regional documents including, but not limited to, Pathways to 30x30, the Natural and Working Lands Climate Smart Strategy, and the San Francisco Bay Area Conservation Lands Network.	Public Agencies, including Joint Power Authorities and Federally-Recognized Indian Tribes Nonprofit organizations with 501(c)(3) status Other community-based organizations and non-federally-recognized tribes may apply with a 501(c)(3) fiscal sponsor	Coastal Conservancy Grants
USDA Natural Resources Conservation Service - Agricultural Conservation Easement Program	This federal program provides funding for two separate programs: 1) Agricultural Land Easements and 2) Wetland Reserve Easements. The Agricultural land easement program protects existing croplands and grasslands from non-agricultural uses through conservation easements and the Wetland program helps to protect, restore and enhance wetlands that have been degraded by agricultural practices	Agricultural Land Easements: Private landowners Tribal landowners Land trusts State and local governments or non-governmental organizations that have existing farmland, rangeland and grassland protection programs Wetland Reserve Easements: Tribal landowners Private landowners	Agricultural Conservation Easement Program

Alameda County Local Agency Formation Commission (Alameda LAFCO) with the support of the Alameda County Resource Conservation District (ACRCD) applied for and received this SALC Land Use Planning grant.

In support of Alameda LAFCO and ACRCD's overlapping interest in conserving agricultural land in Alameda County, the project had three related goals:

1. Identification and Preservation of Agricultural Lands including Working Lands through identification of priority and critical areas for conservation.
2. Infill Development Focused on Healthy and Resilient Communities for Disadvantaged and Low-Income Populations by supporting urban agriculture and community gardens within city limits.
3. Reduce Greenhouse Gas Emissions by finding new areas for urban and rural agriculture in close proximity to residential areas, and conserving agricultural areas that reinforce urban growth boundaries both with land conservation measures, like easements, and with sustainable economic opportunities for agricultural producers.

To help you evaluate the characteristics of the County relative to different planning and policy goals, a number of data layers have been brought together from existing data sources (see About the Data below). To allow the layers to be considered together, they have been spatially resampled into hexagonal planning units with an area of 160 acres, and scaled from 0-1. These units were constructed specifically for this Decision Support Tool for the purposes of combining the values of different data layers, and are not actual units used in standard planning processes. When viewing the map, you can adjust the weight of these layers on a scale of 0-5, which defines how much they contribute to the color of each hexagon.

For many planning purposes, you don't need to view all layers at once. Preset groups of layers have been prepared for common use cases, i.e., common land acquisition or conservation grants. For example, if you are interested in seeing which areas of the county are most valuable for wildlife conservation, you can select the "WCB" preset which will display 7 layers related to wildlife.

• General information: The color-coding and composite numbers of each hexagon on the map are created by adding together the values of the weights for selected variable layers to create a composite score between 0 (for example, land that is not prioritized for agricultural conservation or development) to 1 (highest priority for agricultural conservation or development). The sum of the variable values is indicated in the planning units (1/4 mile hexagons). Planning units that are more conducive to agriculture (as determined by the user's weighting of the variable layers) or the grant aims are closer to 1 and more green whereas variable values that are less prioritized are closer to 0 and more brown. The user can decide the importance of each visible variable by using the slider. By default, all variables are set to 1 at the start, indicating all variables in the set are of equal importance. Variables can be deemed unimportant or unnecessary to the composite score by sliding the value to 0. The maximum value for the variables is 5.


• Looking at the opening page, the top row of tabs can be used to navigate between the map ("Agricultural Land Prioritization Tool"), information about the raw data layers that were used to create the map ("Data Atlas"), if/how data were manipulated to be used in the map and models ("Documentation"), and a place where the user can submit feedback about the maps once they are fully functional ("Feedback").


• In the left bar of the "Agricultural Land Prioritization Tool" tab there are 4 subsections. Clicking on the "eye" icon next to "Places, Roads and Highways" will turn off and on the city names, roads and highways. Clicking the "eye" icon next to "Basic Info" will turn some informational layers on and off. You can click the "arrow" icon to the left of "Basic Info" to view the individual layers and turn them on and off as well using their individual "eye" icons. Once clicked, the list icon to the right of each Basic Info layer will show you the legend, but only if the layer has been turned on by clicking the "eye". The fourth subsection is where the user can build grant or other priority specific models. Clicking on the "eye" icon next to "Planning Units" will turn the modeled hexagons on and off. You can also click the list icon to the right of "Planning Units" to reveal the model color values e.g., hexagons that are more green indicate a higher priority for grant priority, whereas hexagons that are more brown indicate the opposite.


• The "Basic Information" subsection contains some variables that might be used for basic planning and/or might not have clear grant priority one way or another. For example, the data layer "Plan Bay Area Growth Geographies." In some cases, areas of planned future development might be a poor choice for agriculture (e.g., might be areas with lots of construction or short-term pollution, parcel size is likely small, etc.) but on the other hand, these might be ideal areas to think about non-traditional agriculture (e.g., rooftop ag) or community gardens, given the likelihood of high housing-density and green space needs in these areas.


• Clicking on the top of the "All Layers" bar will reveal the grants that can currently be modeled in the Agricultural Land Prioritization Tool, plus a list of all variables used in All grants (currently titled "All Layers") which might be used for non-listed grant opportunities or for your own organizational planning purposes. There are currently 4 grants we have included. We have chosen specific variables for each grant based on each grants' guidelines and data availability. To view a short summary of each grant you can hover over the "i" icon next to each grant title. When using the map to identify the value of a known parcel (uploaded by the user) to each grant, it is important to carefully choose the value of each variable. You can learn more information about each variable's relationship to agricultural value and/or grant priority by hovering over the "i" icon next to each variable. By clicking on the "i" icon next to each variable you can see a description of the underlying data source, including the year it was published, a link to view it yourself, and the grant categories it was included in. Some variables were included in all grant categories (labeled in the pop-up as "All Grants"), however, most were included in 1-3 grant groups. Lastly, you can zoom in to specific areas across the map to see composite priority scores overlaid on each hexagon.


• There are 4 icons within the map box. The + will zoom into the map whereas the - will zoom out. The house icon will bring you back to the standard map view where all of Alameda County is visible on the screen. The cloud with an arrow icon allows the user to upload their own zipped shapefile onto the map. KML and KMZ files are not supported. Once uploaded, a fourth subsection will appear on the left column called "Uploaded Shapefile". You can turn the highlighted area from shapefile on and off by clicking the "eye" icon to the left of the uploaded layer. This function was created specifically to allow for land trusts and other conserving bodies to be able to view and weigh variables of interests in a parcel of land in question—for a grant or other purpose—and preserve the general privacy of landholders in Alameda County.

If you are interested in finding land parcels in Alameda County you can visit the Alameda County Parcel Data Portal, where you will be able to search by address or filter by APN. You can download individual parcels as shapefiles, CSVs, and KMZs by toggling the filter button and selecting "download". If you choose to download your parcels in a shapefile format, you can upload the zipped folder directly to the web tool.

Agricultural Land Prioritization Tool Hexagons (Informatics and GIS, UC ANR)
2023
The hexagonal planning units layer used in the Agricultural Land Prioritization Tool. Each field in the layer has values derived from each of the layers described below and summarized by the planning units, or hexagons.

For more questions regarding access to and information about this layer, please reach out to: igis@ucanr.edu

Alameda County Boundary (Alameda County Open Data Portal)
2023
An Alameda County boundary maintained by the Alameda County Information Technology Department, last updated June 2, 2023.

Areas with Active Farming or Grazing (LandIQ; California Department of Water Resources)
In Prioritization Tool: Areas with Active Grazing/Farming 2022All grants
A layer categorizing land-cover types in California, created by LandIQ in conjunction with the California Department of Water Resources, and last updated in August of 2022.

More Details

We selected land-cover types relevant to this tool and divided them into two categories and layers: Farming and Grazing. The farming, or crop-related, classes included Citrus and Subtropical, Deciduous, Field Crops, Grain and Hay, Truck Crops, Vineyard, and Young Perennial. The grazing layer included lands categorized as either Grazing or Pasture. We applied the same workflow for assigning both the Farming and Grazing layers to the Alameda County Hexagons. First, we utilized the Union tool in ArcGIS Pro to intersect the layers and their underlying data with the hexagons. The Union tool allowed us to see which hexagons did and did not intersect with the layers. Hexagons that had no overlap with the layers were automatically selected out and given a score of zero because areas without active farming or grazing are not likely to be suitable for future farming and grazing activities. Hexagons that did intersect with the layers were given a score of 1 because areas with active farming or grazing are more likely to be suitable for future farming and grazing activities. The final result was two hexagon layers, with each hexagon given a score of 1 or 0 depending on whether or not they intersected with the farming or grazing layers.

Census Tracts with Households Making Below 80% of Median Household Income (California Energy Commission)
In Prioritization Tool: Low Income 2023SALC grants
A layer published by the California Energy Commission detailing census tracts where median household incomes are at or below 80% of the statewide median income. According to the Department of Housing and Community Development, census tracts where median household incomes are at or below 80% of the statewide median income are designated as low-income. This layer was last updated in July of 2023.

More Details

Each polygon in the data layer represents a census tract where median household incomes are at or below 80% of the statewide median income. Originally this data layer covered the entirety of the state of California; however, using the Clip tool in ArcGIS Pro, we confined the area to census tracts within Alameda County. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with the layer were automatically selected out and given a score of 0. Hexagons that did intersect with the polygon were assigned a decimal value between 0-1 based on the share of each hexagon that did intersect with the census tracts. For example, if a hexagon intersected completely with a low-income census tract then that hexagon would be given a linear normalized score of 1, equivalent to 100%. Similarly, if only 50% of a hexagon intersected with a census tract then that hexagon would be given a linear normalized score of 0.50. Hexagons with higher scores are prioritized in this web app because low median incomes are often correlated with other community disadvantages, and these areas should be considered as priority areas for green spaces, including urban farms and gardens.

Conservation Easements (GreenInfo Network)
In Prioritization Tool: Areas Near Conservation Easements 2022Basic Info
A layer published by GreenInfo Network as part of their California Conservation Easement Database (CCED). The database contains lands in California that are protected under conservation easements. CCED acknowledges that there are easement data and information gaps in this dataset. To report missing easements, you can email cced@calands.org. This layer was last updated in December of 2022.

More Details

Each polygon in the data layer represents a conservation easement documented in the CCED. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with the layer were automatically selected out and given a score of zero. Hexagons that did intersect with the polygon were assigned a decimal value between 0-1 based on the share of each hexagon that did intersect with the easements. For example, if a hexagon intersected completely with easements, then that hexagon would be given a linear normalized score of 1, equivalent to 100%. If only 25% of a hexagon intersected with easements, then that hexagon would be given a linear normalized score of 0.25. Hexagons with higher scores are prioritized in this web app because protecting areas near conservation easements may better preserve larger, contiguous spaces.

Parks and Other Protected Areas (GreenInfo Network)
In Prioritization Tool: Areas Near Parks and Other Protected Areas 2022Basic Info
A layer published by GreenInfo Network as part of their California Protected Areas Database (CPAD). The database contains 15,000 parks and preserves in California protected as open space. However, protection does not ensure that all lands under CPAD are protected for biodiversity or other specific conservation activities. According to CPAD, "it means that these lands are owned by agencies whose general mission is to continue the open space uses on them". This caveat includes lands utilized for multiple uses and lands subject to extracve use (e.g. mining or logging). CPAD does not include military lands, tribal lands, private golf courses, and public lands not intended for open space, such as administrative buildings and municipal waste facilities. This layer was updated in December of 2022.

More Details

Each polygon in the data layer represents a protected park or preserve documented in the CPAD. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with the layer were automatically selected out and given a score of zero. Hexagons that did intersect with the polygon were assigned a decimal value between 0-1 based on the share of each hexagon that did intersect with the protected areas. For example, if a hexagon intersected completely with protected areas, then that hexagon would be given a linear normalized score of 1, equivalent to 100%. If only 25% of a hexagon intersected with easements, then that hexagon would be given a linear normalized score of 0.25. Hexagons with higher scores are prioritized in this web app because protecting areas near parks and preserves may better preserve larger, contiguous spaces.

Sustainable Groundwater Management Act (SGMA) Water Basin Priority(CA Dept. of Water Resources)
In Prioritization Tool: Areas with Sufficient Water Basins 2019SALC grants
A layer published by the California Department of Water Resources (CDWR) detailing basin prioritization throughout the State of California required by the Sustainable Groundwater Management Act (SGMA) of 2019. 517 groundwater basins and subbasins are categorized as being either high, medium, low, or very low priority based on present population overlying the basin, the number of wells that draw from the basin, documented impacts on the groundwater within the basin, the irrigated acreage overlying the basin, among other considerations. This data layer was created in 2019.

More Details

Each polygon in the data layer represents a groundwater basin or subbasin within Alameda County that has been classified as being Low, Very Low, Medium or High priority. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with the layer were automatically given a score of zero. Hexagons that intersected with a very low, low, medium or high priority basin were given normalized linear scores of 1, 0.75, 0.5, and 0.25, respectively. In some cases, the hexagons intersected basins with a combination of 2 or more basin priority classifications. In these cases, the priority classification of the largest basin area intersecting with the hexagon was assigned to that hexagon. For example, if 75% of one hexagon intersected with a low priority basin and 25% of with a medium priority basin, the hexagon would be given a score of 0.75. Hexagons with higher scores are prioritized in this web app because areas with sufficient water basins can be better relied upon for agricultural use, such as irrigated crops.

Buffer Zone: Outside City Limits and inside Sphere of Influence (Informatics and GIS, UC ANR)
In Prioritization Tool: Outside City Limits and inside Sphere of Influence 2022SALC grantsBasic Info
A layer created by IGIS depicting the areas outside of Alameda County limits but within each city's Sphere of Influence. This layer was created in 2022.

More Details

Each polygon in this data layer represents a buffer zone outside the city limits and inside the Sphere of Influence. For this layer, we utilized the Distance Allocation tool in ArcGIS Pro to create a distance raster. Smaller distance values represented closer proximity to the buffer zone and larger values represented farther proximity to the buffer zone, with the Alameda County boundary as the extent. Next, we ran the Zonal Statistics tool, with the Alameda County Hexagons layer as the Feature Zone parameter, to summarize these distance values by the hexagons. Because each cell from the distance raster was smaller than each of the individual hexagons, we decided to take the mean value of the raster cells within a given hexagon and attribute them to each hexagon. At this point, each hexagon had a mean distance value, where smaller mean distance values represented closer proximities to buffer zones and larger mean distance values represented farther proximities to buffer zones. The ultimate goal of this layer was to give higher values to areas near buffer zones because areas outside of city limits but within the sphere of influence of a city are more likely to be developed and thus have a greater risk of conversion to non-agricultural uses. To prioritize city limit proximity we needed areas within and nearby buffer zones to have linear normalized values moving towards 1, and areas increasingly further away from city boundaries to have linear normalized values moving towards 0. To achieve this result we took the mean distance assigned to each hexagon, divided it by the maximum of all mean values, subtracted that value by 1 and then took the absolute value so that we wouldn't have any negative distance values. In the final result of this layer, hexagons within the buffer boundaries were given scores of 1, hexagons moving away from the boundaries were given scores linearly decreasing from 1, and the furthest hexagon from all buffer boundaries in Alameda County was given a score of 0.

California Urban Growth Scenarios for 2050 (Landis; California Resources Agency, Legacy Project)
In Prioritization Tool: Urban Areas in 2050 2015SALC grantsNRCS - AEP grants
A layer published by Landis and the California Resources Agency presenting the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2050. Updated in 2015, these projections are based on extrapolations of current population trends and recent urban development trends.

More Details

Each polygon in the data layer represents a continuous urban growth boundary. Within Alameda County alone, there are 82 individual boundaries with varying sizes. We utilized the union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with the layer were given a score of zero. Hexagons that did overlap with the polygons were assigned a linear normalized score between 0-1 based on what percentage of their total area intersected with the growth boundary. For example, if 60% of a hexagon intersected with a growth boundary then that hexagon would be given a linear normalized score of 0.6. We chose to have these hexagon values be continuous along the edges of the growth boundaries to account for the variability of urban spread and to ensure prioritization could extend beyond presently urbanized areas. Areas with higher hexagon values are most likely to experience growth in the future and therefore, any potential or current agricultural areas are at risk of conversion within the next few decades.

San Francisco Estuary Institute (SFEI) Wetlands (San Francisco Estuary Institute)
In Prioritization Tool: Wetlands 2023WCB grantsCCC grants
A data layer published by the San Francisco Estuary Institute cataloging surface waters and their riparian areas across California that are standardized to a common wetland classification system. The CARI dataset combines wetland information from a variety of sources, including the National Wetlands Inventory, the National Hydrography Dataset and the State Wetland and Riparian Area Monitoring Plan. The dataset was created to assist with landscape-level analyses of surface waters and their riparian areas on wide-ranging scales. It was last updated in April of 2023.

More Details

Each polygon in the data layer represents a unique wetland in Alameda County, categorized into one of fourteen wetland types seen in the area. We utilized the Union tool in ArcGIS Pro to intersect the wetlands layer with the hexagons. Hexagons that had no overlap with the layer were given a score of zero. Hexagons that did intersect with the layers were given a normalized score of 1. We decided to prioritize hexagons that overlapped with wetlands because wetland areas are crucial both for species biodiversity and as important flood buffers. Although some wetland areas in the dataset were much smaller than the size of a given hexagon, we still wanted to prioritize hexagons with even a small amount of overlap because the areas surrounding wetlands are also likely beneficial for biodiversity and buffering floods.

City Limits (Alameda County Open Data Portal)
In Prioritization Tool: City Limits 2022NRCS - AEP grantsBasic Info
A layer of Alameda County incorporated city limits maintained by the Alameda County Information Technology Department, last updated April 25, 2022.

More Details

Each polygon in this data layer represents an incorporated city within Alameda County. For this layer, we utilized the Distance Allocation tool in ArcGIS Pro to create a distance raster. Smaller distance values represented closer proximity to the city limits and larger values represented farther proximity to the city limits, with the Alameda County boundary as the extent. Next, we ran the Zonal Statistics tool, with the Alameda County Hexagons layer as the Feature Zone parameter, to summarize these distance values by the hexagons. Because each cell from the distance raster was smaller than each of the individual hexagons, we decided to take the mean value of the raster cells within a given hexagon and attribute them to each hexagon. At this point, each hexagon had a mean distance value, where smaller mean distance values represented closer proximities to city limits and larger mean distance values represented farther proximities to city limits. The ultimate goal of this layer was to give higher values to areas near city limits because these protections may reinforce and protect those limits in the future. To prioritize city limit proximity we needed areas within and nearby city limits to have linear normalized values moving towards 1 and areas increasingly further away from city boundaries to have linear normalized values moving towards 0. To achieve this result we took the mean distance assigned to each hexagon, divided it by the maximum of all mean values, subtracted that value by 1 and then took the absolute value so that we wouldn't have any negative numbers. In the final result of this layer, hexagons within the city boundaries were given scores of 1, hexagons moving away from the boundaries were given scores linearly decreasing from 1, and the furthest hexagon from all city boundaries in Alameda County was given a score of 0.

Critical Wildlife Habitat (United States Fish and Wildlife Service)
In Prioritization Tool: Critical Wildlife Habitat 2023All grants
A layer published by the United States Fish and Wildlife Service detailing geographic areas that include characteristics necessary to conserve threatened and endangered species and that may need extra management and protection. Critical wildlife habitat can include areas not currently inhabited by the species of interest, but will likely need them for recovery. This dataset was last updated in February of 2023.

More Details

This dataset covers the entirety of the United States, but for the purpose of this web app we clipped the data to only include critical wildlife habitats in Alameda County. The goal of this layer was to prioritize lands that are occupied or could be occupied by threatened or endangered species. We utilized the Union Tool in ArcGIS Pro to intersect this data layer with the hexagons. Hexagons that had no overlap with the critical wildlife habitat polygons were given a score of zero. Hexagons that overlapped less than 50% with the critical habitat polygons were also given a score of 0. Meanwhile, hexagons that completely overlapped or overlapped more than 50% with the critical habitat polygons were given a score of 1.

Farmers Markets Locations (Alameda County Community Development Agency; The Ecology Center)
In Prioritization Tool: Areas Within 2 Miles of Farmers Markets 2022All grants
A map and data layer published by the Alameda County Community Development Agency and maintained by the Ecology Center showcasing certified farmers market locations in Alameda County and the San Francisco Bay Area at large. This data was last updated in 2022.

More Details

Each point in the dataset represents a certified farmers market location in the San Francisco Bay Area, however, we only selected locations in Alameda County. For the purpose of this web app, we wanted to prioritize areas within 2 miles of farmers markets because of the possible connections between local communities and the opportunities for synergies with new urban agriculture locations.We utilized the Path Distance tool in ArcGIS Pro to create a buffer of 2 miles surrounding each farmers market site. The tool uses street and road networks to calculate the set distance parameter and produces a raster where each cell contains the distance moving away from the point. Next, we ran the Zonal Statistics tool, with the Alameda County Hexagons layer as the Feature Zone parameter, to summarize the distance values by the hexagons. Hexagons that did not fall within 2 miles of the sites were given a normalized score of 0, as they were not seen as priorities for this analysis. All hexagons that either landed within 2 miles of the sites or had some overlap with the distance boundary were given a score of 1. We made the decision to give hexagons with only some overlap a score of 1 for this layer too because they still communicated a relative proximity to the sites given that each hexagon is 0.25 mile squared in area.

Greatest Need to Address Food Insecurity (Healthy Alameda County)
In Prioritization Tool: Food Insecurity 2022SALC grants
A layer using data from Healthy Alameda County's Food Insecurity Index. The index grades each zip code or census tract based on the amount of need necessary to address food insecurity. A score of 1 corresponds to least food insecure areas (and less need) and a score of 5 corresponds to most food insecure areas (and more need). The index uses data from 2022.

More Details

The food insecurity data from Healthy Alameda County, grouped by census tracts, was downloaded in a tabular format. We joined the data table to a polygon with all the census tracts for Alameda County so that we could incorporate it into our web app spatially. At this point, each census tract had a Food Insecurity Index score, ranked from 1-5. We then utilized the Union tool in ArcGIS Pro to intersect the layer with the Alameda County Hexagons. Hexagons that did not intersect with the data layer, either because they were in the bay or overlapped with census tracts containing No Data, were given a score of zero. For hexagons that did overlap with the census tracts, we calculated the percent area each Alameda County hexagon intersected with a given Food Insecurity Index grade. Whichever ranking (1-5) had the most overlap with our hexagons was assigned that score. For example, if Hexagon X intersected 40% with a census tract ranked 3 and 60% with a census tract ranked 4, then Hexagon X would be assigned a ranking of 4. Overall, hexagons with a food insecurity ranking of 5 were normalized to 1; hexagons with a ranking of 4 were normalized to 0.8; hexagons with a ranking of 3 were normalized to 0.6; hexagons with a ranking of 2 were normalized to 0.4; hexagons with a ranking of 1 were normalized to 0.2; and lastly, hexagons with a ranking of zero were normalized to 0. We chose to preserve the categorical rankings of the Food Insecurity Index because they were determined by combining a variety of metrics, including medicaid insurance enrollment, perceived health status, household expenditures, household income, and single-parent headed households. Prioritizing census tracts with greater need to address food insecurity is a priority of this web app because these areas could benefit from urban agricultural activities.

Bay Area Habitat and Wildlife Linkages (Science & Collaboration for Connected Wildlands)
In Prioritization Tool: Habitat Linkages 2014WCB grantsCCC grants
A layer published by Science & Collaboration for Connected Wildlands that identifies lands that are deemed essential to promote functional connectivity among wildlands and their associated species and ecological processes. The "landscape blocks", as they are referred to in the layer's metadata, are landscapes of significant ecological importance that form crucial wildlife movement connections. This data was last updated in 2014.

More Details

Each polygon in the dataset represents a unique landscape block for the San Francisco Bay Area. For the purpose of this web app, we used the Clip tool in ArcGIS Pro to select for the landscape blocks that overlapped with Alameda County. We then utilized the Union tool in ArcGIS pro to intersect the layer with the Alameda County Hexagons. Hexagons that did not intersect with the data layer were given a normalized score of zero. Hexagons that fully intersected with the landscape blocks were given a normalized score of 1. Hexagons that saw over 50% of their area overlap with the layer were given a score of 1 as well. Hexagons that saw less than 50% of their area overlap with the layer were given a score of zero. Prioritizing hexagons intersecting with the landscape blocks can be seen as a tool to mitigate urban sprawl, preserve large contiguous spaces and reduce the likelihood of land conversion.

Urban Agriculture Sites in UCANR Database (UC Agriculture and Natural Resources)
In Prioritization Tool: Known Urban Ag Sites within 2 Miles 2020SALC grantsBasic Info
A map and data layer published by UC Agriculture and Natural Resources showcasing urban agriculture sites in the San Francisco Bay Area, including urban farms, and school and community gardens. This data was last updated in 2020.

More Details

Each point in the dataset represents an urban agriculture site in the San Francisco Bay Area, however, we only selected sites within Alameda County for this analysis. For the purpose of this web app, we wanted to prioritize areas within 2 miles of urban agriculture sites in order to promote nearby infill and foster connections between local communities. We utilized the Path Distance tool in ArcGIS Pro to create a buffer of 2 miles surrounding each urban agriculture site. The tool uses street and road networks to calculate the set distance parameter and produces a raster where each cell contains the distance moving away from the point. Next, we ran the Zonal Statistics tool, with the Alameda County Hexagons layer as the Feature Zone parameter, to summarize the distance values by the hexagons. Hexagons that did not fall within 2 miles of the urban agriculture sites were given a normalized score of 0, as they were not seen as priorities for this analysis. All hexagons that either landed within 2 miles of the sites or had some overlap with the distance boundary were given a score of 1. We made the decision to give hexagons with only some overlap a score of 1 for this layer too because they still communicated a relative proximity to the sites given that each hexagon is 0.25 mile squared in area.

Plan Bay Area 2050 Growth Geographies (Metropolitan Transportation Commission; Association of Bay Area Governments Executive Board)
In Prioritization Tool: Plan Bay Area growth geographies 2021SALC grantsBasic Info
A layer published by MTC and the ABAG Executive Board detailing Priority Development Areas, Priority Production Areas, Transit Rich Areas, and High Resource Areas as part of Plan Bay Area (PBA) 2050. These areas were designated in 2020 as priorities for new housing and jobs in the PBA and are largely selected by local jurisdictions. This data layer was last updated in 2021.

More Details

Each polygon in the dataset represents an individual growth geography labeled as either a Priority Development Area, Priority Production Area, Transit Rich Area, or High Resource Area. The data layer was restricted to Growth Geographies within Alameda County using the Clip tool in ArcGIS Pro. We then utilized the Union tool in ArcGIS Pro to intersect the layer with the Alameda County hexagons. Hexagons that did not intersect with the data layer were given a score of zero. Hexagons that completely overlapped with the data layer were given a maximum normalized score of 1. Hexagons that did overlap with the polygons were assigned a linear normalized score between 0-1 based on what percentage of their total area intersected with the Growth Geographies. For example, if 60% of a hexagon intersected with a specific growth boundary then that hexagon would be given a linear normalized score of 0.6. We chose to have these hexagon values be continuous along the edges of the growth geography boundaries to maintain prioritization near areas of interest and create buffers where opportunity for small-scale urban or community agriculture space could still exist.

Plan Bay Area 2050 Priority Conservation Areas (Metropolitan Transportation Commission; Association of Bay Area Governments Executive Board)
2023Basic Info
A layer published by MTC and the ABAG Executive Board detailing Priority Conservation Areas (PCAs). PCAs are areas designated with the purpose of protecting natural lands and open spaces across the San Francisco Bay Area. These conservation areas include farms, ranches, and recreational and resource lands. The areas are sites from property owners, open space districts, land trusts, local jurisdictions and more. This layer is only included in the Basic Information part of the web app.

Soil Organic Carbon (United States Geological Survey; Geospatial Innovation Facility)
In Prioritization Tool: Predicted soil carbon stocks 2018SALC grantsNRCS - AEP grants
A data layer published by the United States Geological Survey (USGS) and UC Berkeley's Geospatial Innovation Facility (GIF) detailing predicted organic soil carbon stocks for the year 2023. The predictions (ranging from 2006-2050) were based on carbon and land-use data at a 1km scale collected by USGS between 1992 and 2005. The units for these predictions are tons per hectare (T ha-1). This dataset was last updated in 2018.

More Details

Each raster cell in this dataset has a value that describes the mass of predicted organic soil carbon in tons per hectare. First, we used the Clip tool in ArcGIS Pro to work only with the data available for Alameda County. Then, we vectorized the dataset using the Raster to Polygon tool, meaning we converted each of the raster cells into polygons while keeping the accompanying attribute information. Next, we utilized the Union tool in ArcGIS pro to intersect the hexagon layer with the organic soil carbon stock polygons. Hexagons that did not intersect with the data layer were given a score of 0. Because multiple hexagons could fit within each soil carbon polygon, we decided to take the maximum value present in the intersection and attribute it to the hexagon. For example, if Hexagon X intersected with both Polygon A and with Polygon B, but Polygon A had a higher value, then Hexagon X would take the value of Polygon A. To normalize the values so that they would be continuous between 0 and 1, we set each hexagon equal to: its present value minus the minimum value of the dataset, divided by the maximum value minus the minimum value. If x is the present value of the hexagon, then the equation would be: (x - min(x)) / (max(x) - min(x)). Higher predicted soil carbon values were prioritized because they contribute to overall carbon neutrality and climate resiliency emphasized by the SALC grant.

Prime Soil for Crop Agriculture (United States Department of Agriculture; Natural Resources Conservation Service)
In Prioritization Tool: Prime Soil 2022SALC grantsNRCS - AEP grants
A layer published by the USDA and NRCS as part of the Soil Survey Geographic Database (SSURGO). This layer details numerical and categorical suitability for agricultural soil based on factors such as soil texture, depth, subsoil properties and surface relief. This dataset specifically covers the entirety of California, but SSURGO layers can be found for each state in the U.S. This data layer was last updated in December of 2022.

More Details

Each polygon in this data represents an area of agricultural land assigned a Storie Index grade. Grade 1 is deemed Excellent; Grade 2 is deemed Good; Grade 3 is deemed Fair; Grade 4 is deemed Poor; Grade 5 is deemed Very Poor; Grade 6 is deemed Non-agricultural; and some lands were categorized as Not Rated or Not Applicable for Storie Index. For the purpose of this web app, we wanted to prioritize lands that were most suitable for agricultural use, and more specifically, irrigated agriculture. We removed polygons with Grades 4-6, as well as polygons Not Rated or Not Applicable for Storie Index to keep the most suitable lands found in Grades 1-3. We utilized the Union Tool in ArcGIS Pro to intersect the Prime Soil layer with the hexagons. Hexagons that did not intersect with the data layer were given a score of zero. Hexagons that did intersect with the data layer were given a normalized maximum score of 1 to demonstrate their agricultural suitability.

Rangeland Priority Conservation Areas (California Rangeland Conservation Coalition)
In Prioritization Tool: Rangeland Priority Conservation Areas 2007SALC grantsCCC grantsNRCS - AEP grants
A layer published by the California Rangeland Conservation Coalition that identifies rangelands in California that are of conservation concern based on vegetation data, stakeholder input, and ongoing conservation efforts. The priority tiers (critical or important priority) in the layer were determined based on the number of times a planning unit was selected as relevant given the inputs listed above. The rangelands included in this analysis exist below 1,500 feet of elevation, as rangelands above this threshold were not thought to be facing the same development pressure at the time. This data layer was last updated in 2007.

More Details

Each polygon in this dataset represents an area assigned a rangeland conservation score, scaled from 0 to 2. A score of 0 equates to no priority, a score of 1 equates to important priority and a score of 2 equates to critical priority. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that did not intersect with the data layer were given a score of 0. Some of the Alameda County hexagons intersected with multiple polygons from the rangeland priority layer. We calculated the percent area each Alameda County hexagon intersected with a given priority ranking. Whichever ranking (important or critical) had the most overlap with our hexagons was assigned that score. For example, if Hexagon X intersected 70% with a polygon ranked 2 and 30% with a polygon ranked 1, then Hexagon X would be assigned a ranking of 2, or critical priority. We decided to preserve the categorial rankings of resiliency from the original layer because they were based on methodical analytical processes. Conserving rangelands as a means of mitigating conversion and urban sprawl is a priority of this web app, and therefore rangelands with a ranking of 2 were normalized to 1; hexagons with a ranking of 1 were normalized to 0.5; and lastly, hexagons that didn't intersect or had no priority were normalized to 0.

Sphere of Influence (SOI) for each jurisdiction (Alameda Local Agency Formation Commission)
In Prioritization Tool: Projected City Spread (Sphere of Influence) 2020SALC grantsNRCS - AEP grants
A layer created by the Alameda Local Agency Formation Commission (LAFCO) that details spheres of influence for cities and special jurisdictions within Alameda County. Spheres of Influence are tools for planning that include the municipal boundaries and service areas of local agencies. This data was last updated in 2020.

More Details

Each polygon in this data layer represents a sphere of influence (SOI) for municipalities within Alameda County. For this layer, we utilized the Distance Allocation tool in ArcGIS Pro to create a distance raster. Smaller distance values represented closer proximity to the SOIs and larger values represented farther proximity to the city SOIs, with the Alameda County boundary as the extent. Next, we ran the Zonal Statistics tool, with the Alameda County Hexagons layer as the Feature Zone parameter, to summarize these distance values by the hexagons. Because each cell from the distance raster was smaller than each of the individual hexagons, we decided to take the mean value of the raster cells within a given hexagon and attribute them to each hexagon. At this point, each hexagon had a mean distance value, where smaller mean distance values represented closer proximities to SOIs and larger mean distance values represented farther proximities to SOIs. The ultimate goal of this layer was to give higher values to areas near SOIs because these protections may reinforce and protect those limits in the future. To prioritize SOI proximity we needed areas within and nearby SOIs to have linear normalized values moving towards 1 and areas increasingly further away from SOI boundaries to have linear normalized values moving towards 0. To achieve this result we took the mean distance assigned to each hexagon, divided it by the maximum of all mean values, subtracted that value by 1 and then took the absolute value so that we wouldn't have any negative numbers. In the final result of this layer, hexagons within the SOIs were given scores of 1, hexagons moving away from the boundaries were given scores linearly decreasing from 1, and the furthest hexagon from all SOI boundaries in Alameda County was given a score of 0.

Terrestrial Climate Change Resilience (California Department of Fish and Wildlife)
In Prioritization Tool: Terrestrial Climate Change Resilience 2021SALC grants
A layer, published by the California Department of Fish and Wildlife through their Areas of Conservation Emphasis Project (ACE). It details the probability that an area within California may serve as refugia from climate change, summarized by hexagonal land units, similar to those in our web app. According to CDFW, these refugia are buffered from the effects of climate change, have conditions that may stay suitable for plants and wildlife, and possess the potential to keep ecological processes relatively intact. Hexagons were then given a score, ranging from 1 to 5 (and 0 for areas with no data), based on the level of resiliency determined in their analysis. The layer was last updated in December of 2021.

More Details

Each polygon in this dataset represents a hexagon assigned a terrestrial climate change resiliency score, scaled from 1 to 5. A score of 1 equates to very low resiliency and a score of 5 equates to very high resiliency. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Because the dataset covered the entire extent of Alameda County, the only instance where the layer did not intersect with the hexagons occurred in the San Francisco Bay and those hexagons were given a score of 0. The hexagons used by CDFW for the purpose of this layer were larger than the ones we used in our analysis. As a result, some of the Alameda County hexagons intersected with multiple hexagons from the terrestrial climate change resiliency layer. We calculated the percent area each Alameda County hexagon intersected with a given resiliency ranking. Whichever ranking (1-5) had the most overlap with our hexagons was assigned that score. For example, if Hexagon X intersected 40% with a polygon ranked 2 and 60% with a polygon ranked 5, then Hexagon X would be assigned a ranking of 5. We decided to preserve the categorial rankings of resiliency from the original layer because they were based on methodical analytical processes. Conserving habitats with greater climate change resiliency is a priority of this web app, and therefore hexagons with a ranking of 5 were normalized to 1; hexagons with a ranking of 4 were normalized to 0.8; hexagons with a ranking of 3 were normalized to 0.6; hexagons with a ranking of 2 were normalized to 0.4; hexagons with a ranking of 1 were normalized to 0.2; and lastly, hexagons with no data were normalized to 0.

Terrestrial Native Wildlife Species Wildlife Richness (California Department of Fish and Wildlife)
In Prioritization Tool: Terrestrial Native Species Wildlife Richness 2021All grants
A layer, published by the California Department of Fish and Wildlife through their Areas of Conservation Emphasis Project (ACE). It details terrestrial native species richness summarized by hexagonal land units, similar to those in our web app. In this layer, native species richness describes a tally of the aggregate number of native terrestrial species thought to be present in each hexagonal unit based on species range and distribution information. Hexagons were then given a score, ranging from 1 to 5, based on these total counts. The layer was last updated in December of 2021.

More Details

Each polygon in this dataset represents a hexagon assigned a terrestrial native species richness score, scaled from 1 to 5. A score of 1 equates to very low species richness and a score of 5 equates to very high species richness. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Because the dataset covered the entire extent of Alameda County, the only instance where the layer did not intersect with the hexagons occurred in the San Francisco Bay and those hexagons were given a score of 0. The hexagons used by CDFW for the purpose of this layer were larger than the ones we used in our analysis. As a result, some of the Alameda County hexagons intersected with multiple hexagons from the species richness layer. We calculated the percent area each Alameda County hexagon intersected with a given species richness ranking. Whichever ranking (1-5) had the most overlap with our hexagons was assigned that score. For example, if Hexagon X intersected 40% with a polygon ranked 2 and 60% with a polygon ranked 5, then Hexagon X would be assigned a ranking of 5. We decided to preserve the categorial rankings of species richness from the original layer because they were based on relevant scientific groupings. Conserving habitats with greater numbers of native species is a priority of this web app, and therefore hexagons with a ranking of 5 were normalized to 1; hexagons with a ranking of 4 were normalized to 0.8; hexagons with a ranking of 3 were normalized to 0.6; hexagons with a ranking of 2 were normalized to 0.4; hexagons with a ranking of 1 were normalized to 0.2; and lastly, hexagons with a ranking of zero were normalized to 0.

Soil Survey Geographic database (SSURGO) Available Water Storage (Natural Resources Conservation Service)
In Prioritization Tool: Water storage capacity of soil 2023SALC grantsWCB grants
A data layer published by the Natural Resources Conservation Service displaying the amount of water the top 150 cm of soil can store and become available to plants. In this layer, available water storage (AWS) was calculated from the difference between soil water content at field capacity and the stable wilting point adjusted for fragments and salinity. This dataset, which has AWS information for the entire United States, was developed to create water budgets, protect water resources, assess a soil's capacity to support crops, among many other uses. This dataset was last updated in June of 2023.

More Details

Each raster cell in this dataset has a value that describes the amount of water in cm that can be stored in the top 150 cm of soil, ranging from 0-150cm. First, we used the Clip tool in ArcGIS Pro to work only with the data available for Alameda County. Then we used the Zonal Statistics tool in ArcGIS pro to summarize the data values from the AWS layer by the Alameda County hexagons. Because multiple raster cells could fit within each hexagon, we decided to take the mean of the AWS values and assign that number to each individual hexagon unit. To normalize the mean AWS values so that they would be continuous between 0 and 1, we set each hexagon equal to: its present value minus the minimum value of the dataset, divided by the maximum value minus the minimum value. If x is the present mean value of the hexagon, then the equation would be: (x - min(x)) / (max(x) - min(x)). This equation gives us continuously normalized values between 0-1, where higher values mean that there is more AWS and lower values mean there is less AWS. Higher AWS values were prioritized because greater water storage capacity in the top 150 centimeters of soil makes the soil more suitable for many types of agriculture.

Williamson Act Parcels (California Department of Conservation)
In Prioritization Tool: Williamson Act 2023SALC grantsBasic Info
A layer published by the California Department of Conservation detailing lands protected by the California Land Conservation Act of 1965, otherwise known as the Williamson Act. The Act allows private landowners and municipal governments to form contractual partnerships for the purpose of restricting specific land parcels to agricultural or public open space use. This data layer summarizes and reflects recent data submitted to the California Department of Conservation by local planning agencies and/or assessor offices throughout the state. The layer is updated regularly, with the most recent changes made in April of 2023.

More Details

Each polygon in this dataset represents an area of land protected under the Williamson Act. We utilized the Union tool in ArcGIS Pro to intersect the layer with the hexagons. Hexagons that had no overlap with Williamson Act parcels were given a score of zero. Hexagons that completely overlapped with the polygons were given a maximum score of 1. Hexagons that did overlap with the polygons were assigned a linear normalized score between 0-1 based on what percentage of their total area intersected with the parcels. For example, if 60% of a hexagon intersected with a Williamson Act parcel then that hexagon would be given a linear normalized score of 0.6. We chose to have these hexagon values be continuous along the edges of the parcel boundaries to maintain prioritization near Williamson Act parcels, which may contribute to preserving larger, contiguous spaces.

This web map was developed by the Alameda County RCD in collaboration with AC LAFCO. The GIS work and website were developed by the Informatics and GIS Statewide Program under the University of California Division of Agriculture and Natural Resources. Funding was provided under a SALC Planning Grant from CDFA.

The authors also greatly acknowledge input from numerous Alameda County organizations and agencies.

---

To properly cite the tool in your work or documentation, please use the following format:

Title of Web Map App: Alameda County Agricultural Resiliency Project (ACARP) Planning Map
Developed By: The Alameda County Resource Conservation District in collaboration with AC LAFCO & the Informatics and GIS Statewide Program under UC ANR
URL: https://geodata.ucanr.edu/acrcd/#

Example: "Agricultural Land Prioritizations were made using spatial insights from the Alameda County Agricultural Resiliency Project (ACARP) Planning Map,
a web-based mapping application developed by ACRCD, AC LAFCO and the IGIS Statewide Program, https://geodata.ucanr.edu/acrcd/#, accessed on [Date]"

We kindly ask that you acknowledge the use of the tool if referencing or incorporating its results into your own work.