The Community Health and Equity Index was developed by Raimi + Associates to compare health conditions, vulnerabilities, and cumulative burdens across the City of Los Angeles. The Index standardizes demographic, socio-economic, health conditions, land use, transportation, food environment, crime, and pollution burden variables, and then averages them together, yielding a score on a scale of 0-100. Lower values indicate better community health.Variables used in the index include: Hardship Index, Life Expectancy, Health Variables (Heart Disease Mortality, Emergency Department Visits for Heart Attacks, Respiratory Disease Mortality, Diabetes Mortality, Stroke Mortality, Childhood Obesity, Percentage of Low Birth Weight Infants, Number of Emergency Department Visits for Asthma for Under 17 and 18+ age groups), Walkability Index, Complete Communities Index (amenities and establishments serving the community), Transportation Index, Modified Retail Food Environment Index, Crime Rate (Violent Crimes, Property Crimes), and Pollution Burden (Pollution Exposure, Environmental Effects).Variables were assigned weights and averaged together. Weights were assigned based on the weights used in the 2013 Health Atlas. For more information, see page 181 of the 2013 Health Atlas, which is available as a PDF on the Los Angeles City Planning website, https://planning.lacity.gov.

The Bergamot Area Plan, adopted September 10, 2013, is a focused planning effort called for in the 2010 LUCE intended to give voice to community preferences for land uses, circulation networks, infrastructure, parking strategies, open space, the arts, and design of buildings in the mixed-use, transit-oriented Bergamot area. The Plan was partially funded by a HUD Community Challenges grant encouraging compact, mixed-use development linked to transit. The Bergamot Area Plan was initiated to help transition 142.5 acres of former industrial land into a walkable, sustainable, and innovative complete neighborhood.

The Communities of Opportunity designated on the Maryland QAP Comprehensive Opportunity Maps are based on a “Composite Opportunity Index” developed by DHCD. The Composite Opportunity Index uses publicly - available data and is based on three major factors: community health, economic opportunity, and educational opportunity. To be designated a Community of Opportunity, and mapped as such to the Maryland QAP Comprehensive Opportunity Maps, the community must have a Composite Opportunity Index that it is above the statewide average. See Section 3.1 of the Program Guide for more details. https://dhcd.maryland.gov/HousingDevelopment/Documents/rhf/2020Guide.pdf

This dataset represents the number of Gateway Patients per the total population of the Census Block Group. The population is the estimate from the American Community Survey for the same time period (2014-2016).

This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The Communities of Opportunity designated on the Maryland QAP Comprehensive Opportunity Maps are based on a 'Composite Opportunity Index' developed by DHCD. The Composite Opportunity Index uses publicly - available data and is based on three major factors: community health - economic opportunity - and educational opportunity. To be designated a Community of Opportunity - and mapped as such to the Maryland QAP Comprehensive Opportunity Maps - the community must have a Composite Opportunity Index that it is above the statewide average. See Section 3.1 of the Program Guide for more details. http://mdhousing.org/Website/Programs/rhf/documents/Guide.pdf Last Updated: 03/2016Feature Service Link:https://mdgeodata.md.gov/imap/rest/services/BusinessEconomy/MD_HousingDesignatedAreas/FeatureServer ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

This map shows the total emergency department visit rate per 10,000 by county. Counties are shaded based on quartile distribution. The lighter shaded counties have lower emergency department visit rates. The darker shaded counties have higher emergency department visit rates. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties, 11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

This layer delineates Development Nodes as developed in the 2022 - 2042 Forsyth County Comprehensive Plan adopted 10/20/2022. Development nodes were identified within each character area. Development nodes area areas where employment, commercial, and higher intensity uses should be concentrated to create regional, community, and neighborhood activity centers. There are three types of nodes, regional, community, and neighborhood. Please see the Forsyth County Website for the entire Comprehensive Plan and a detailed description of the Development Nodes.Comprehensive Plan (2022 - 2042) The purpose of the Comprehensive Plan is to guide the intensity, location and timing of development and to ensure compatibility with existing uses, infrastructure and economic trends while protecting natural and cultural resources.Forsyth County's Comprehensive Plan serves as a policy guide as decisions are made in relation to growth and land use change. The plan addresses critical issues and opportunities through the incorporation of a shared vision for the community's future.Please see the Forsyth County website for a complete copy of the plan as well as a .PDF of the Community Character Map.

A. SUMMARY This dataset includes COVID-19 tests by resident neighborhood and specimen collection date (the day the test was collected). Specifically, this dataset includes tests of San Francisco residents who listed a San Francisco home address at the time of testing. These resident addresses were then geo-located and mapped to neighborhoods. The resident address associated with each test is hand-entered and susceptible to errors, therefore neighborhood data should be interpreted as an approximation, not a precise nor comprehensive total.

In recent months, about 5% of tests are missing addresses and therefore cannot be included in any neighborhood totals. In earlier months, more tests were missing address data. Because of this high percentage of tests missing resident address data, this neighborhood testing data for March, April, and May should be interpreted with caution (see below)

Percentage of tests missing address information, by month in 2020 Mar - 33.6% Apr - 25.9% May - 11.1% Jun - 7.2% Jul - 5.8% Aug - 5.4% Sep - 5.1% Oct (Oct 1-12) - 5.1%

To protect the privacy of residents, the City does not disclose the number of tests in neighborhoods with resident populations of fewer than 1,000 people. These neighborhoods are omitted from the data (they include Golden Gate Park, John McLaren Park, and Lands End).

Tests for residents that listed a Skilled Nursing Facility as their home address are not included in this neighborhood-level testing data. Skilled Nursing Facilities have required and repeated testing of residents, which would change neighborhood trends and not reflect the broader neighborhood's testing data.

This data was de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected).

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco. During this investigation, some test results are found to be for persons living outside of San Francisco and some people in San Francisco may be tested multiple times (which is common). To see the number of new confirmed cases by neighborhood, reference this map: https://sf.gov/data/covid-19-case-maps#new-cases-maps

B. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information. All testing data is then geo-coded by resident address. Then data is aggregated by analysis neighborhood and specimen collection date.

Data are prepared by close of business Monday through Saturday for public display.

C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure.

D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a data user can analyze this data by "specimen_collection_date".

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. Percent positivity indicates how widespread COVID-19 is in San Francisco and it helps public health officials determine if we are testing enough given the number of people who are testing positive. When there are fewer than 20 positives tests for a given neighborhood and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for neighborhood by the total number of residents who live in that neighborhood (included in the dataset), then multiply by 10,000. When there are fewer than 20 total tests for a given neighborhood and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

E. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing cleaning efforts.
• 1/31/2023 - updated “acs_population” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - implemented system updates to streamline and improve our geo-coded data, resulting in small shifts in our testing data by geography.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 1/31/2023 - removed the “multipolygon” column. To access the multipolygon geometry column for each geography unit, refer to COVID-19 Cases and Deaths Summarized by Geography.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

From February to October 2016, the American Red Cross and its local Red Cross partners completed an effort to extensively map areas within a 15-kilometer distance of the shared borders between Guinea, Liberia, and Sierra Leone.

The goal of this work was to create an open and comprehensive dataset of communities for West Africa and to ensure that decision makers, humanitarian workers, and community stakeholders are better aware of water, sanitation, health, and community resources before and during the next crisis.

To complete this mapping, the American Red Cross launched a mapping center in Guéckédou, Guinea, and used it as both a base of operations and a community engagement facility. Over 100 volunteers helped to complete a rapid assessment of the region, visiting over 7,000 communities by motorbike to complete a vulnerability survey with the village leader. Next, over 100 communities were selected for a round of detailed mapping, focusing on collecting the location and information about every water point, health facility and other community resource in the area. In addition, we led technical skills trainings and mapping events both in Guéckédou and across the region.

ALL DATA EXCEPT FOR THE OpenStreetMap EXTRACTS ARE LICENSED AS CC-BY 4.0

The IndieWeb is a people-focused alternative to the "corporate" web. Participants use their own personal web sites to post, reply, share, organize events and RSVP, and interact in online social networking in ways that have otherwise been limited to centralized silos like Facebook and Twitter.

The Indie Map dataset is a social network of the 2300 most active IndieWeb sites, including all connections between sites and number of links in each direction, broken down by type. It includes:

• 5.8M web pages, including raw HTML, parsed microformats2, and extracted links with metadata.
• 631M links and 706K "friend" relationships between sites.
• 380GB of HTML and HTTP requests in WARC format.

The zip file here contains a JSON file for each site, which includes metadata, a list of other sites linked to and from, and the number of links of each type.

The complete dataset of 5.8M HTML pages is available in a publicly accessible Google BigQuery dataset. The raw pages can also be downloaded as WARC files. They're hosted on Google Cloud Storage.

More details in the full documentation.

Indie Map is free, open source, and placed into the public domain via CC0. Crawled content remains the property of each site's owner and author, and subject to their existing copyrights.

The Tamalpais Lands Collaborative (One Tam; https://www.onetam.org/), the network of organizations that manage lands on Mount Tamalpais in Marin County, initiated the countywide mapping project with their interest in creating a seamless, comprehensive map depicting vegetation communities across the landscape. With support from their non-profit partner the Golden Gate National Parks Conservancy (https://www.parksconservancy.org/) One Tam was able to build a consortium to fund and implement the countywide fine scale vegetation map.Development of the Marin fine-scale vegetation map was managed by the Golden Gate National Parks Conservancy and staffed by personnel from Tukman Geospatial (https://tukmangeospatial.com/) Aerial Information Systems (AIS; http://www.aisgis.com/), and Kass Green and Associates. The fine-scale vegetation map effort included field surveys by a team of trained botanists. Data from these surveys, combined with older surveys from previous efforts, were analyzed by the California Native Plant Society (CNPS) Vegetation Program (https://www.cnps.org/vegetation) with support from the California Department of Fish and Wildlife Vegetation Classification and Mapping Program (VegCAMP; https://wildlife.ca.gov/Data/VegCAMP) to develop a Marin County-specific vegetation classification.High density lidar data was obtained countywide in the early winter of 2019 to support the project. The lidar point cloud, and many of its derivatives, were used extensively during the process of developing the fine-scale vegetation and habitat map. The lidar data was used in conjunction with optical data. Optical data used throughout the project included 6-inch resolution airborne 4-band imagery collected in the summer of 2018, as well as 6-inch imagery from 2014 and various dates of National Agriculture Imagery Program (NAIP) imagery.In 2019, a 26-class lifeform map was produced which serves as the foundation for the much more floristically detailed fine-scale vegetation and habitat map. The lifeform map was developed using expert systems rulesets in Trimble Ecognition®, followed by manual editing.In 2019, Tukman Geospatial staff and partners conducted countywide reconnaissance fieldwork to support fine-scale mapping. Field-collected data were used to train automated machine learning algorithms, which produced a fully automated countywide fine-scale vegetation and habitat map. Throughout 2020, AIS manually edited the fine-scale maps, and Tukman Geospatial and AIS went to the field for validation trips to inform and improve the manual editing process. In the spring of 2021, draft maps were distributed and reviewed by Marin County's community of land managers and by the funders of the project. Input from these groups was used to further refine the map. The countywide fine-scale vegetation map and related data products were made public in June 2021. In total, 107 vegetation classes were mapped with a minimum mapping size of one fifth to one acre, varying by class.Accuracy assessment plot data were collected in 2019, 2020, and 2021. Accuracy assessment results were compiled and analyzed in the summer of 2021. Overall accuracy of the lifeformmap is 95%. Overall accuracy of the fine-scale vegetation map is 77%, with an overall 'fuzzy' accuracy of 81%.The Marin County fine-scale vegetation map was designed for a broad audience for use at many floristic and spatial scales. At its most floristically resolute scale, the fine-scale vegetation map depicts the landscape at the National Vegetation Classification alliance level - which characterizes stands of vegetation generally by the dominant species present. This product is useful to managers interested in specific information about vegetation composition. For those interested in general land use and land cover, the lifeform map may be more appropriate. Tomake the information contained in the map accessible to the most users, the vegetation map is published as a suite of GIS deliverables available in a number of formats. Map products are being made available wherever possible by the project stakeholders, including the regional data portal Pacific Veg Map (http://pacificvegmap.org/data-downloads).

Compilation on 01/07/2021 of the inventories of rivers returned by the local managers of the Morbihan SAGES department — authorities — administrations. Complete mapping is a map layer of all watercourses identified and characterised according to the criteria set out in Article.L.215-7-1 of the Environmental Code A stream is a flow of running water into a natural bed originally fed a source and having a sufficient flow rate. The Compilation as of 01/07/2021 of the inventories of rivers returned by the local managers of the Morbihan SAGES department — communities — administrations. Complete mapping is a map layer of all watercourses identified and characterised according to the criteria set out in Article.L.215-7-1 of the Environmental Code

Update: Bike Action Plan Amendment was adopted unanimously by City Council on 10/13/20The 2011 Bike Action plan provided a 5-year and 20-year road map for developing Santa Monica’s bicycle network. At that time protected bicycle lanes were not yet well-known in the United States. This Amendment provides an update to the Bike Action Plan that builds on the 20-year vision by updating corridors to protected bike lanes. The Amendment was developed following the same goals and objectives of the Bike Action Plan, but with an updated approach to meet the needs of Santa Monica today. The Amendment creates a path forward for Santa Monica to build a network of protected bikeways citywide in the next five years, compete for outside grant funding, and to continue progress towards the community’s climate, safety, and mobility goals, and build resilience in these uncertain economic times.2011 Bike Action PlanThe Bike Action Plan is guided and supported by Santa Monica’s award-winning 2010 Land Use and Circulation Element (LUCE) which lays out a bold vision for the city’s future, one that protects and enhances the city’s beautiful neighborhoods, creates new community benefits in complete neighborhoods around the new light rail stations, supports community character through good design, and minimizes traffic through a “No Net New Vehicle Trips” policy. This Bike Action Plan strives to be equally bold and practical. On the one hand, this plan envisions a future Santa Monica in which it is attractive and fun for Santa Monicans of all ages and abilities to use a bike to get everywhere in the city and to meet all the needs of daily life. On the other hand, it is also a detailed five-year implementation strategy for moving toward that vision. The adopted LUCE established a strong framework that supports the Bike Action Plan through:Integrating Land Use and TransportationCreating Complete StreetsPreserving and Enhancing NeighborhoodsManaging CongestionEnsuring Quality Transportation ChoicesFacilitating Affordable and Healthy TransportationSupporting Economic Health

Comprehensive Plan Community Meeting Comments.This data layer is used by the Generalized Future Land Use application.

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C , X-SAR and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on September 2, 1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994.

Vegetation mapping will monitor the spatial extent, pattern, and proportion of plant communities within major landscape regions of the Greater Everglades Wetlands. Specific landscape changes to be monitored that pertain to the CERP include the following: · Changes in the extent and orientation of sloughs, tree islands, and sawgrass ridges as flow patterns, flow volumes, hydroperiods, and water quality are modified in the ridge and slough landscape · Changes in the extent and distribution of cattail as flow patterns, flow volumes, hydroperiods, and water quality are modified in the ridge and slough landscape · Changes in the extent and distribution of exotic plant communities · Changes in the distribution and configuration of tidal creeks, salt marshes, and mangrove forests as changing flow patterns and volumes interact with sea level and salinity in the mangrove estuaries of Florida Bay and the Gulf of Mexico · Changes in the distribution of plant communities in calcitic wetlands, including tussock-forming Muhlenbergia and sawgrass communities in the major breeding locations of the Cape Sable seaside sparrow, as hydrologic gradients change · Changes in the distribution of plant communities of eastern Big Cypress with the removal of L-28 and hydroperiod restoration in the Kissimmee Billy Strand Regional landscape patterns will be monitored using a combination of a transect and sentinel site sampling design (Section 3.1.3.1) and a stratified random sampling design (Section 3.1.3.10). Aerial photo-interpretation is currently the best tool available to produce dependable and accurate maps of the Everglades (Welch et al. 1995, Doren et al. 1999, Rutchey and Vilchek 1999, Richardson and Harris 1995). Aerial photography of the greater Everglades wetland system at a scale of 1/24,000 will be purchased at three-year intervals. Photography will be interpreted and ground-truthed to produce vegetation maps at three-year intervals for the randomly selected cells. Additional cells will be mapped to supplement the stratified random cells along the alignments of the coastal, marl prairie -slough, and WCA gradients that are described above. The vegetation classification scheme of Jones et al. (unpublished report) will be used to identify major plant communities that are defined by typical dominant species.

This dataset is a comprehensive inventory of Alaskan buildings, storage tanks, and roads that were: (1) detected from 0.5 meter resolution satellite imagery of communities (acquired between 2018-2023) and (2) supplemented by OpenStreetMap data. We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery across the Arctic region. Shapefiles beginning with "HABITAT_AK" contain only the post-processed deep learning predictions. Shapefiles beginning with "HABITAT_OSM" contain the post-processed deep learning predictions supplemented by OpenStreetMap data. The HABITAT pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover. Finally, we selected buildings and roads from the OpenStreetMap Alaska dataset (downloaded in June 2024 from https://download.geofabrik.de/) that do not intersect with any deep learning predictions to generate a merged OSM and HABITAT infrastructure dataset. This merged product comprises a total building footprint of 53 million square meters and a road network of 63,744 km across the state of Alaska.

The Census Bureau released revised delineations for urban areas on December 29, 2022. The new criteria (contained in this Federal Register Notice) is based primarily on housing unit density measured at the census block level. The minimum qualifying threshold for inclusion as an urban area is an area that contains at least 2,000 housing units or has a population of at least 5,000 persons. It also eliminates the classification of areas as “urban clusters/urbanized areas”. This represents a change from 2010, where urban areas were defined as areas consisting of 50,000 people or more and urban clusters consisted of at least 2,500 people but less than 50,000 people with at least 1,500 people living outside of group quarters. Due to the new population thresholds for urban areas, 36 urban clusters in California are no longer considered urban areas, leaving California with 193 urban areas after the new criteria was implemented.

The State of California experienced an increase of 1,885,884 in the total urban population, or 5.3%. However, the total urban area population as a percentage of the California total population went down from 95% to 94.2%. For more information about the mapped data, download the Excel spreadsheet here.

Please note that some of the 2020 urban areas have different names or additional place names as a result of the inclusion of housing unit counts as secondary naming criteria.

Please note there are four urban areas that cross state boundaries in Arizona and Nevada. For 2010, only the parts within California are displayed on the map; however, the population and housing estimates represent the entirety of the urban areas. For 2020, the population and housing unit estimates pertains to the areas within California only.

Data for this web application was derived from the 2010 and 2020 Censuses (2010 and 2020 Census Blocks, 2020 Urban Areas, and Counties) and the 2016-2020 American Community Survey (2010 -Urban Areas) and can be found at data.census.gov.

For more information about the urban area delineations, visit the Census Bureau's Urban and Rural webpage and FAQ.

To view more data from the State of California Department of Finance, visit the Demographic Research Unit Data Hub.

A household consists of all the people occupying a housing unit. A household includes related and unrelated persons, if any, such as lodgers, foster children, wards, or employees who share the housing unit. A person living alone in a housing unit, or a group of unrelated people sharing a housing unit such as partners or roomers, is also counted as a household. The count of households excludes group quarters. Source: U.S. Bureau of the Census, American Community Survey Years Available: 2010, 2015-2019

ECM Community Support Services tables for a Quarterly Implementation Report. Including the County and Plan Details for both ECM and Community Support.This Medi-Cal Enhanced Care Management (ECM) and Community Supports Calendar Year Quarterly Implementation Report provides a comprehensive overview of ECM and Community Supports implementation in the programs' first year. It includes data at the state, county, and plan levels on total members served, utilization, and provider networks.ECM is a statewide MCP benefit that provides person-centered, community-based care management to the highest need members. The Department of Health Care Services (DHCS) and its MCP partners began implementing ECM in phases by Populations of Focus (POFs), with the first three POFs launching statewide in CY 2022.Community Supports are services that address members’ health-related social needs and help them avoid higher, costlier levels of care. Although it is optional for MCPs to offer these services, every Medi-Cal MCP offered Community Supports in 2022, and at least two Community Supports services were offered and available in every county by the end of the year.