Description: The neighborhoods shown in this dataset are derived from a larger dataset drawn and maintained by the Data Desk, a team of Times reporters and Web developers in downtown L.A. The boundaries have expanded and shifted over time and now cover all of Los Angeles County broken down into 272 neighborhoods.This version of the LA Times boundaries only includes neighborhoods fully or partially within the City of Los Angeles. Neighborhoods that extend into other cities have been clipped to only show the portion(s) of the neighborhoods that are within the City of Los Angeles.Data Source: Los Angeles Times' Mapping LA project.Last Updated: October 7, 2016Refresh Rate: Never - Historical data (Note: should the LA Times update their Mapping LA project with new boundaries in the future, a new LA-specific layer will be added to the GeoHub as well.)

A neighborhood is generally defined spatially as a specific geographic area and functionally as a set of social networks. Neighborhoods, then, are the spatial units in which face-to-face social interactions occur—the personal settings and situations where residents seek to realize common values, socialize youth, and maintain effective social control.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

PDF Map of the Neighborhood Association boundaries for the City of Santa Ana, California. Last updated May 7, 2020.

Mapping of neighborhoods in Quebec City.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we mapped the landscape of financial services providers and advertisements in low-income neighborhoods in Orange County. Through documenting the presence of alternative financial services (AFS) providers and fringe financial advertisements, alongside traditional financial services providers, we investigated the spatial relationship between these businesses, as well as the factors that create consumers’ sense of (dis)trust in them. This data set contains photographs taken as part of this mapping research. All study materials and procedures were approved by the University of California, Irvine Office of..., Data was collected over the course of five trips throughout Orange County, California, between November 2021 and February 2022, yielding 420 photographs. Areas of focus were determined by utilizing the 2019 Family Financial Stability Index (FFSI; Parsons et al.), a multivariate metric developed for Orange County United Way to measure the financial stability of families with children under 18. Each trip, researchers navigated to financial services providers in neighborhoods of low family financial stability. In addition to photographing these providers, researchers drove block-by-block through the area and documented traditional and fringe financial advertisements found on telephone poles, billboards, bus shelters, and the like. Photographs were only taken in public spaces of material in plain view., Photographs are organized in folders according to trip (labeled A through E). Each photo is labeled by the trip and a number (e.g. “TripX_AdMapping_X.jpeg†). The photo directory associated with each trip contains the photo file names, descriptions and notes, and type (billboard, storefront, phone pole ad, etc.). Trip A took place in southern Santa Ana and western Orange; trip B was in northern Santa Ana and southern Anaheim; trip C was in northern Anaheim, Placentia, and Fullerton; trip D was in western Anaheim and northern Garden Grove; and trip E was in western Anaheim, northern Garden Grove, and Westminster. A map of Orange County coded according to the FFSI is included in the supplemental information (where red and dark orange indicate a neighborhood with a low score). The map also identifies local credit unions, community research partners, alternative financial services providers, and a selection of photographs from the mapping research.,

Neighborhood boundaries for the City of Long Beach were established on November 18, 2021 by the Long Beach Independent Redistricting Commission.

Socio-demographic and delivery characteristics of births by HOLC grade In San Francisco, Oakland and Los Angeles California.

This polygon feature class contains polygons outlining the neighbourhoods within The City of Pickering.

Maintenance and Update Frequency: As needed

Department: City DevelopmentThe City of Pickering does not make any representations concerning the accuracy, likely results or reliability of the use of the materials. The City hereby disclaims all representations and warranties. While great care will be taken to keep this information as up to date as possible we cannot guarantee complete accuracy. For additional details please refer to the most up to date version of The City of Pickering's Licence Agreement.

Context

This is modified dataset combining MCI and Homicide

Sources

Unemployment rate: https://www.statista.com/statistics/578362/unemployment-rate-canada/

Police data: http://data.torontopolice.on.ca/pages/open-data

Population data and analysis: see percapita.ipynb Population of toronto: https://en.wikipedia.org/wiki/Demographics_of_Toronto

For 2017: https://www.google.com/publicdata

For 2018: https://www.toronto.ca/city-government/data-research-maps/toronto-at-a-glance/

Average Income: Canadian Mortgage and Housing Company, https://www.cmhc-schl.gc.ca/en/corp/about/cahoob/data/upload/Table-17_EN_Before-Tax_w.xlsx

Neighborhoods map data https://www.toronto.ca/city-government/data-research-maps/open-data/

The practice of redlining was codified by a series of maps created as part of the New Deal by the Home Owners’ Loan Corporation, which evaluated the mortgage lending risk of neighborhoods.

As this is an external map, the 'Updated' date on this page does not reflect when the data was actually updated. This data is updated weekly (Monday before start of business).

The data provides geospatial information for parcels of land within Edmonton's mature neighbourhoods that have been approved for subdivision or are currently in review for a subdivision application.

The City of Edmonton has shifted the delivery of legal and title parcel mapping to the provincial government and their designated partners. As of November 1st, 2021, the City will no longer provide datasets with land parcel boundary polygon geometry. Alberta Data Partnerships (ADP) - https://abdatapartnerships.ca/ - and their joint venture partner AltaLIS - https://www.altalis.com - now have the responsibility for making this information available for most of the province, including within the City of Edmonton.

Please see our Digital Map Products website https://www.edmonton.ca/business_economy/purchase_maps_aerial_photographs/digital-products for more information.

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.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The Census Bureau established blocks covering the entire nation for the first time in 1990.There are less number of Census Blocks within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared in 2010.Updated:1. June 2023: This update includes 2022 November Santa Clarita City annexation and the addition of "Kinneloa Mesa" community (was a part of unincorporated East Pasadena). Added new data fields FIP_CURRENT to CITYCOMM_CURRENT to reflect new/updated city and communities. Updated city/community names and FIP codes of census blocks that are in 2022 November Santa Clarita City annexation and new Kinneloa Mesa community (look for FIP_Current, City_Current, Comm_Current field values)2. February 2023: Updated few Census Block CSA values based on Demographic Consultant inquiry/suggestions3. April 2022: Updated Census Block data attribute values based on Supervisorial District 2021, Service Planning Area 2022, Health District 2022 and ZIP Code Tabulation Area 2020Created: March 2021How This Data is Created? This census geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/ on February 2021 and customized for LA County. New data fields are added in the census blocks 2020 data and populated with city/community names, LA County FIPS, 2021 Supervisorial Districts, 2020 Census Zip Code Tabulation Area (ZCTA) and some administrative boundary information such as 2022 Health Districts and 2022 Service Planning Areas (SPS) are also added. "Housing20" field value and "Pop20" field value is populated with PL 94-171 Redistricting Data Summary File: Decennial Census P.L. 94-171 Redistricting Data Summary Files. Similarly, "Feat_Type" field is added and populated with water, ocean and land values. Five new data fields (FIP_CURRENT to CITYCOMM_CURRENT) are added in June 2023 updates to accommodate 2022 Santa Clarita city annexation. City/community names and FIP codes of census blocks affected by 2022 November Santa Clarita City annexation are assigned based on the location of block centroids. In June 2023 update, total of 36 blocks assigned to the City of Santa Clarita that were in Unincorporated Valencia and Castaic. Note: This data includes 3 NM ocean (FEAT_TYPE field). However, user can use a definition query to remove those. Data Fields: 1. STATE (STATEFP20): State FIP, "06" for California, 2. COUNTY (COUNTYFP20): County FIP "037" for Los Angeles County, 3. CT20: (TRACTCE20): 6-digit census tract number, 4. BG20: 7-digit block group number, 5. CB20 (BLOCKCE20): 4-digit census block number, 6. CTCB20: Combination of CT20 and CB20, 7. FEAT_TYPE: Land use types such as water bodies, ocean (3 NM ocean) or land, 8. FIP20: Los Angeles County FIP code, 9. BGFIP20: Combination of BG20 and FIP20, 10. CITY: Incorporated city name, 11. COMM: Unincorporated area community name and LA City neighborhood, also known as "CSA", 12. CITYCOMM: City/Community name label, 13. ZCTA20: Parcel specific zip codes, 14. HD12: 2012 Health District number, 15. HD_NAME: Health District name, 16. SPA22: 2022 Service Planning Area number, 17. SPA_NAME: Service Planning Area name, 18. SUP21: 2021 Supervisorial District number, 19. SUP_LABEL: Supervisorial District label, 20. POP20: 2020 Population (PL 94-171 Redistricting Data Summary File - Total Population), 21. HOUSING20: 2020 housing (PL 94-171 Redistricting Data Summary File - Total Housing),22. FIP_CURRENT: Los Angeles County 2023 FIP code, as of June 2023,23. BG20FIP_CURRENT: Combination of BG20 and 2023 FIP, as of June 2023,24. CITY_CURRENT: 2023 Incorporated city name, as of June 2023,25. COMM_CURRENT: 2023 Unincorporated area community name and LA City neighborhood, also known as "CSA", as of June 2023,26. CITYCOMM_CURRENT: 2023 City/Community name label, as of June 2023.

Toronto Neighbourhoods Boundary File includes Crime Data by Neighbourhood. Counts are available at the offence and/or victim level for Assault, Auto Theft, Bike Theft, Break and Enter, Robbery, Theft Over, Homicide, Shootings and Theft from Motor Vehicle. Data also includes crime rates per 100,000 people by neighbourhood based on each year's Projected Population by Environics Analytics.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario..In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

Street segments and intersections that have been paved within the last 5 yearsData pushed to ArcGIS Online on November 9, 2025 at 3:05 AM by SFGIS.Data from: https://data.sfgov.org/d/5wbp-dwztDescription of dataset columns:

 CNN
 CNN of street segment or intersection


 Streetname
 Street name


 Limits
 A description of location combining from_st, to_st, cardinal and addrange


 JobOrderNum
 Job Order Number - a code for funding - under which street was paved


 EffectiveDate
 Date Location was paved


 ExpirationDate
 End of utility excavtion moratorium for location


 ModifiedDate
 date record was added or modified


 ID
 Unique ID


 X
 CA State Plane III


 Y
 CA State Plane III


 Latitude
 WGS84


 Longitude
 WGS84


 Location
 Location formatted for mapping


 point



 Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'Neighborhoods' (jwn9-ihcz) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 SF Find Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'SF Find Neighborhoods' (6qbp-sg9q) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Police Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Police Districts' (qgnn-b9vv) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Supervisor Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Supervisor Districts' (26cr-cadq) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Analysis Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'Analysis Neighborhoods' (ajp5-b2md) the point in column 'point' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

Descriptions of Metropolitan Transportation Commission's 34 Super DistrictsSuper District #1 - Greater Downtown San Francisco: This area, the northeastern quadrant of the city, is bounded by Van Ness Avenue on the west, 11th Street on the southwest, and Townsend Street on the south. This Super District includes the following neighborhoods and districts: Financial District, Union Square, Tenderloin, Civic Center, South of Market, South Park, Rincon Hill, Chinatown, Jackson Square, Telegraph Hill, North Beach, Nob Hill, Russian Hill, Polk Gulch and Fisherman's Wharf. Treasure Island and Yerba Buena Island are also part of Super District #1.Super District #2 - Richmond District: This area, the northwestern quadrant of the city, is bounded by Van Ness Avenue on the east, Market Street on the southeast, and 17th Street, Stanyan Street, and Lincoln Way on the south. Super District #2 includes the following neighborhoods and districts: the Presidio, the Western Addition District, the Marina, Cow Hollow, Pacific Heights, Cathedral Hill, Japantown, Hayes Valley, Duboce Triangle, the Haight-Ashbury, the Richmond District, Inner Richmond, Outer Richmond, Laurel Heights, Sea Cliff, and the Golden Gate Park.Super District #3 - Mission District: This area, the southeastern quadrant of the city, is bounded by Townsend Street, 11th Street, Market Street, 17th Street, Stanyan Street, and Lincoln Way on the northern boundary; 7th Avenue, Laguna Honda, Woodside Avenue, O'Shaughnessy Boulevard and other smaller streets (Juanita, Casita, El Verano, Ashton, Orizaba) on the western boundary; and by the San Mateo County line on the southern boundary. Super District #3 includes the following neighborhoods and districts: China Basin, Potrero Hill, Inner Mission, Outer Mission, Twin Peaks, Parnassus Heights, Dolores Heights, Castro, Eureka Valley, Noe Valley, Bernal Heights, Glen Park, Ingleside, Ocean View, the Excelsior, Crocker-Amazon, Visitacion Valley, Portola, Bayview, and Hunters Point.Super District #4 - Sunset District: This area, the southwestern quadrant of the city, is bounded by Lincoln Way (Golden Gate Park) on the north; 7th Avenue, Laguna Honda, Woodside Avenue, O'Shaughnessy Boulevard and other smaller streets (Juanita, Casita, El Verano, Ashton, Orizaba) on the eastern boundary; and by the San Mateo County line on the southern boundary. Super District #4 includes the following neighborhoods and districts: Inner Sunset, the Sunset District, Sunset Heights, Parkside, Lake Merced District, Park-Merced, Ingleside Heights, West Portal and St. Francis Wood.Super District #5 - Daly City/San Bruno: This northern San Mateo County Super District includes the communities of Daly City, Colma, Brisbane, South San Francisco, Pacifica, San Bruno, Millbrae, and the north part of Burlingame. The boundary between Super District #5 and Super District #6 is Broadway, Carmelita Avenue, El Camino Real, Easton Drive, the Hillsborough / Burlingame city limits, Interstate 280, Skyline Boulevard, the Pacifica city limits, and the Montara Mountain ridgeline extending to Devil's Slide on the coast.Super District #6 - San Mateo/Burlingame: The central San Mateo County Super District includes the communities of Hillsborough, San Mateo, Foster City, Belmont, the southern part of Burlingame, and the coastside communities of Montara, Moss Beach, El Granada, and Half Moon Bay. The southern boundary of Super District #6 is the Foster City city limits, the Belmont/San Carlos city limits, Interstate 280, Kings Mountain, Lobitos Creek extending to Martins Beach on the coast.Super District #7 - Redwood City/Menlo Park: The southern San Mateo County Super District includes the communities of San Carlos, Redwood Shores, Redwood City, Atherton, Menlo Park, East Palo Alto, Woodside, Portola Valley, and the coastside communities of San Gregorio and Pescadero.Super District #8 - Palo Alto/Los Altos: This Santa Clara County Super District includes the communities of Palo Alto, Los Altos, Los Altos Hills, and the western part of Mountain View. Boundaries include the San Mateo County line, US-101 on the north, and Cal-85 (Stevens Creek Freeway) and Stevens Creek on the east.Super District #9 - Sunnyvale/Mountain View: This is the "Silicon Valley" Super District and includes the communities of Mountain View (eastern part and shoreline), Sunnyvale, Santa Clara (northern part), Alviso, and San Jose (northern part). Also included in this Super District is the "Golden Triangle" district. Super District #9 is bounded by US-101, Cal-85, Stevens Creek on the western boundary; Homestead Road on the southern boundary; Pierce Street, Civic Center Drive and the SP tracks in Santa Clara City; and Interstate 880 as the eastern boundary.Super District #10 - Cupertino/Saratoga: This Super District is located in south central Santa Clara County and includes the communities of Cupertino, Saratoga, Santa Clara City (southern part), Campbell (western part), San Jose (western part), Monte Sereno, Los Gatos and Redwood Estates. This area is bounded by Stevens Creek and the Santa Cruz Mountains on the west, Homestead Road on the north, Interstate 880/California Route 17 on the east; Union Avenue, Camden Avenue and Hicks Road (San Jose) also on the eastern boundary; and the Santa Clara/Santa Cruz county line on the south.Super District #11 - Central San Jose: This central Santa Clara County Super District is comprised of San Jose (central area), Santa Clara City (downtown area), and Campbell (east of Cal-17). The general boundaries of Super District #11 are Interstate 880/California Route 17 on the west; US-101 on the east; and the Capitol Expressway, Hillsdale Avenue, Camden Avenue, and Union Avenue on the south boundary.Super District #12 - Milpitas/East San Jose: This eastern Santa Clara County Super District includes the City of Milpitas, and the East San Jose communities of Berryessa, Alum Rock, and Evergreen. Boundaries include Interstate 880 and US-101 freeways on the west; San Jose City limits (Evergreen) on the south; and the mountains on the east.Super District #13 - South San Jose: This south-central Santa Clara County Super District includes the southern part of San Jose including the Almaden and Santa Teresa neighborhoods. Super District #13 is surrounded by Super District #10 on the west; Super District #11 on the north; Super District #12 on the northeast; and Super District #14 on the south at Metcalf Road (Coyote).Super District #14 - Gilroy/Morgan Hill: This area of Santa Clara County is also known as "South County" and includes the communities of Gilroy, Morgan Hill, San Martin and the Coyote Valley. Also included in this Super District are Loma Prieta (western boundary of the Super District) and Mount Hamilton in the northeastern, rural portion of Santa Clara County. This area is bounded by Santa Cruz and San Benito Counties on the south, and Merced and Stanislaus Counties on the eastern border.Super District #15 - Livermore/Pleasanton: This is the eastern Alameda County Super District including the Livermore and Amador Valley communities of Livermore, Pleasanton, Dublin, San Ramon Village, and Sunol. This Super District includes all of eastern Alameda County east of Pleasanton Ridge and Dublin Canyon.Super District #16 - Fremont/Union City: The southern Alameda County Super District includes the communities of Fremont, Newark and Union City. The boundaries for this Super District are the Hayward/Union City city limits on the north side; the hills to the immediate east; the Santa Clara/Alameda County line on the south; and the San Francisco Bay on the west.Super District #17 - Hayward/San Leandro: This southern Alameda County Super District includes the communities of Hayward, San Lorenzo, San Leandro, Castro Valley, Cherryland, and Ashland. The northern border is the San Leandro/Oakland city limits.Super District #18 - Oakland/Alameda: This northern Alameda County Super District includes the island city of Alameda, Oakland, and Piedmont. The Oakland neighborhoods of North Oakland and Rockridge are in the adjacent Super District #19. The border between Super Districts #18 and #19 are the Oakland/Emeryville city limits; 52nd and 51st Streets; Broadway; and Old Tunnel Road.Super District #19 - Berkeley/Albany: This northern Alameda County Super District includes all of Emeryville, Berkeley, and Albany, and the Oakland neighborhoods in North Oakland and Rockridge. The Super District is surrounded by the Alameda/Contra Costa County lines; the San Francisco Bay; and the Oakland Super District.Super District #20 - Richmond/El Cerrito: This is the western Contra Costa Super District. It includes the communities of Richmond, El Cerrito, Kensington, Richmond Heights, San Pablo, El Sobrante, Pinole, Hercules, Rodeo, Crockett, and Port Costa. The eastern boundary to Super District #20 is defined as the Carquinez Scenic Drive (east of Port Costa); McEwen Road; California Route 4; Alhambra Valley Road; Briones Road through the Regional Park; Bear Creek Road; and Wildcat Canyon Road to the Alameda/Contra Costa County line.Super District #21 - Concord/Martinez: This is one of three central Contra Costa County Super Districts. Super District #21 includes the communities of Concord, Martinez, Pleasant Hill, Clayton, and Pacheco. This area is bounded by Suisun Bay on the north; Willow Pass and Marsh Creek on the east; Mt Diablo on the southeast; and Cowell Road, Treat Boulevard, Oak Grove Road, Minert Road, Bancroft Road, Oak Park Boulevard, Putnam Boulevard, Geary Road, and Pleasant Hill Road on the south; and Briones Park, Alhambra Valley Road and Cal-4 on the west.Super District #22 - Walnut Creek: This central Contra Costa County Super District includes the communities of Walnut Creek, Lafayette, Moraga and Orinda. The latter three communities are more popularly known as Lamorinda. The border with Super District #23 generally follows the southern city limits of Walnut Creek.Super