WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:

• Metadata is missing or incomplete for some layers at this time and will be continuously improved.
  
• We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.

Purpose

County and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers (this dataset)
   • Without Coastal Buffers
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system
• Place Name: CDTFA incorporated (city) or county name
• County: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Accuracy

CDTFA"s source data notes the following about accuracy:

City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated

The data for this analysis was obtained through a UC-Davis Coursera course as ElectionData2012.gdb, with polygon layers Counties and PrecinctVotingData. Both of those were loaded into a blank map document, followed by the World Light Grey Canvas basemap.

Then, the author conducted a Spatial Join of the PrecinctVotingData layers TO the Counties layer (target layer). A right click on the fields total_votes and proposition_37_yes_votes enabled the execution of a Sum merge operation for those fields.

After the spatial join, the author went into the Properties of the Join layer, selected Symbology, used the quantity gradient, selected sum_proposition_37_yes-votes as the field for symbology and normalized by the sum_total_votes field. Further, the author formatted the symbology such that the data was represented as a percentage (of the sum_total_votes) and used only 1 decimal place.

The author then went into the Label s tab of the Properties window, chose the County label style for the NAMES field, and edited the label to have a 1-pt. halo around the county names, centered on their feature.

From the attribute table of the Join, the author right-clicked the "sum_total_votes" and the "sum_proposition_37_yes_votes" fields and used the statistics function to gather the sum of the YES votes and the sum of the total votes for the state as a whole, for use in the final, shared map. Revisions were also made to layer names for the benefit of the final map.

The way to access Layers Quickly.

Quick Layers is an Add-In for ArcMap 10.6+ that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 1.164

To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers) using GDRS Manager.

Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

Installation:

After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
1. Open ArcMap
2. Customize -> Add-In Manager… -> Options
3. Click add folder, and enter the location of the Quick Layers app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers
4. After you do this, the Quick Layers toolbar will be available. To add it, go to Customize -> Toolbars and select DNR Quick Layers 10

The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose
County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers (this dataset)
   • Without Coastal Buffers
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)
   • 
     

Point of Contact

California Department of Technology, Office of Digital Services, gis@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.

Purpose

County boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.

This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This layer removes the coastal buffer polygons. This feature layer is for public use.

Related Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers (this dataset)
3. Cities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.
   • With Coastal Buffers
   • Without Coastal Buffers
4. City and County Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, gis@state.ca.gov

Field and Abbreviation Definitions

• CDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.
• CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.
• CENSUS_GEOID: numeric geographic identifiers from the US Census Bureau
• CENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.
• GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.
• CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.
• CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.
• AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.
• OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".
• PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or county
• CENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.
• GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.fed.us/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described belowRECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area.RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. Metadata

The way to access Layers Quickly.

Quick Layers is an Add-In for ArcGIS Pro 3 that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 3.11

To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers for ArcGIS Pro 3) using GDRS Manager.

Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

Installation:

After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
1. Open ArcGIS Pro
2. Project -> Add-In Manager -> Options
3. Click add folder, and enter the location of the Quick Layers Pro app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers_pro3
4. After you do this, the Quick Layers ribbon will be available. To also add Quick Layers to the Quick Access Toolbar at the top, right click Quick Layers, and select Add to Quick Access Toolbar

The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

Riparian corridors are important areas that maintain connectivity throughout the state of California. The riparian corridors complement the northern Sierra Nevada foothills wildlife connectivity project linkages to further achieve connectivity in the study area. We identified 280 riparian corridors represented by 232 named creeks, 43 named rivers, and 5 sloughs, forks or runs. The major corridors are the Sacramento, San Joaquin, Pit, Tuolumne, Merced, Feather and Stanislaus rivers. The 280 riparian corridors connect 201 landscape blocks. The riparian corridors complement the focal species linkages by providing many east-west corridors while the majority of linkages have a north-south orientation. Also by following the entire passage of the riparian area, these corridors run through many of the landscape blocks across the study area, helping to provide connectivity outside of habitat patch areas.We identified riparian corridors by selected streams, rivers and creeks from the NHD (National Hydrography Dataset) for state of California. From the NHD dataset, features named ‘StreamRiver’ were extracted from the ‘NHDFlowline’ vector dataset. A code 46006 was then used to extract perennial rivers and streams from the ‘StreamRiver’ dataset. However, this step resulted in a stream and river layer with many small segments. In order to reduce the number of segments and identify complete stream/river lines, we intersected the perennial rivers and streams layer with the CDFW statewide streams layer (‘CA_Streams_Statewide’) using the ‘Select by Location’ tool in ArcMap (‘CA_Streams_Statewide’ layer as target layer and the streams and rivers layer we extracted from NHD as a target layer). Second, we extracted features named ‘ArtificialPath’ from the ‘NHDFlowline’ vector dataset. Artificial paths represent the flow of water into, through, and out of features delineated using area; for example, rivers wide enough to be delineated as a polygon are represented by an artificial path flowline at their center line. Therefore, large rivers are often coded as “artificial path” in the NHD dataset. We then selected only those artificial paths with Geographic Names Information System (GNIS) names, with the assumption that artificial path features without names are “very minor streams, only of use to hydrologist” (http://nhd.usgs.gov). Next we used the same method we implemented for streams and rivers in order to remove small segments and have complete lines. The artificial path dataset is not coded to discriminate between perennial and intermittent ones similar to stream and river features. As a result, artificial paths that intersected with perennial streams and rivers were selected to represent permanent waterways. Then, the perennial stream and river layer and the artificial paths layer were merged into one dataset. After the merge we added a 500 m buffer to each side of the riparian area.We compared this merged stream/river layer with riparian vegetation classification data as a cross check. The riparian vegetation classification data are from the 2011 Northern Sierra Nevada Foothills and 2013 Eastern Central Valley fine-scale vegetation maps developed by the Vegetation Classification and Mapping Program (VegCamp) at the California Department of Fish and Wildlife. For areas outside the foothills and eastern central valley we used land cover data compiled by California Department of Forestry and Fire Protection (CDF) Fire and Resource Assessment Program (FRAP) in 2006, representing data for the period between 1997 and 2002. The resulting perennial dataset was then merged with the wetland and riparian datasets to represent perennial water sources in California. For more information see the project report at [https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=85358].

The FirePerimeter polygon layer represents daily and final mapped wildland fire perimeters. Incidents of 10 acres or greater in size are expected. Incidents smaller than 10 acres in size may also be included. Data are maintained at the Forest/District level, or their equivalent, to track the area affected by wildland fire. Records in FirePerimeter include perimeters for wildland fires that have corresponding records in FIRESTAT, which is the authoritative data source for all wildland fire reports. FIRESTAT, the Fire Statistics System computer application, required by the USFS for all wildland fire occurrences on National Forest System Lands or National Forest-protected lands, is used to enter and maintain information from the Individual Fire Report (FS-5100-29).

This Esri supported add-in is supported in ArcMap Desktop 10.4 and higher, and used to:1. Update the position of fabric points, using the geometry of features in a reference layer that you configure.2. Merge multiple close fabric points to a specific location that you define.The Fabric Point Move to Feature add-in provides methods to update the positions of parcel points based on feature geometry locations. Feature layers are used as a target reference, and contain the features that are used to update the fabric points.Fabric points can be updated using either a line layer or a point layer.For a demonstration of how to use this tool, please see the Help video available from the toolbar, or directly from here.The source code is available on GitHub.Installing a different version of an add-in.If you are installing the add-in directly on your client machine, as opposed to placing the add-in file at a network share location, then follow these steps:First un-install the version currently on the client machine. 1. In ArcMap go to Customize -> Add-in Manager2. On the Add-ins tab click to select the add-in you want to un-install, and then click the Delete button.3. Click Yes on the dialog that asks for confirmation on the delete.4. Click Close.5. Close ArcMap.6. Start ArcMap and use Add-in Manager to confirm the add-in is not listed under the My Add-ins section of the left pane.7. Close ArcMap.8. Double-click the add-in file for the version of the add-in that you want to install.9. Click the Install Add-in button.10. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under My Add-ins.Troubleshooting Notes: A. if problems are encountered when attemping to run the add-in, check to make sure you have privileges on the well-known folder. You should be able to browse to the file add-in location on disk, in the well-known folder: C:\Users<username>\Documents\ArcGIS\AddIns\Desktop10.<0-1>\B. Alternatively, consider using a network share for your add-in, and follow the steps below.If you use a network share to load the add-in, then follow these steps:1. In ArcMap go to Customize -> Add-in Manager.2. In the left pane on the Add-ins tab, scroll down to the Shared Add-ins.3. Under Shared Add-ins, click on the add-in name that you want to change and confirm the add-in version in the right pane is the one you want to change from.4. Click the Options tab on the Add-in Manager and get the share location for the add-in you want to change from.4. Click Close on the Add-in Manager and close ArcMap.5. Using the required privileges, browse to the share location and replace the add-in file with the version of the add-in file that you want to change to.6. Start ArcMap and use Add-in Manager to confirm that the desired version of the add-in is now listed under Shared Add-ins.General notes and resources:A. See the Administrator Settings heading under the help section here: https://bit.ly/2XD5mb8B. Additional uninstall and re-install steps: https://bit.ly/2xN8dPy

[Metadata] County parks for the counties of Hawaii and Honolulu. Other counties will be added as they are received. Hawaii Statewide GIS Program staff merged the two layers and kept common fields (TMK, park name). Individual county layers with additional information can still be found in the State GIS geoportal (https://geoportal.hawaii.gov/). Dates of data: County of Hawaii: March 2023; County of Honolulu: October 2021. Information for Honolulu/Oahu Parks: Parks, open spaces and outdoor recreational faciilities managed and maintained by the City and County of Honolulu as of October, 2021. Downloaded by the Hawaii Statewide GIS Program from the Honolulu Open Geospatial Data Portal (https://honolulu-cchnl.opendata.arcgis.com/), October 6, 2021. Information for Hawaii County Parks: Parks, open spaces and outdoor recreational facilities managed and maintained by the County of Hawaii. Dataset created by the Department of Research and Development; received by the Hawaii Statewide GIS Program March 2023. This dataset features Parks and Recreation locations on Hawaii Island. Note: Parks locations joined with dataset: TMK Parcel Boundaries for the County of Hawaii as of April, 2022. Source: County of Hawaii.For more information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/parks_county.pdf or contact the Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

Pursuant to Section 7-100l of the Connecticut General Statutes, each municipality is required to transmit a digital parcel file and an accompanying assessor’s database file (known as a CAMA report), to its respective regional council of governments (COG) by May 1 annually. The dataset has combined the Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2025 into a single dataset. This dataset is designed to make it easier for stakeholders and the GIS community to use and access the information as a geospatial dataset. Included in this dataset are geometries for all 169 municipalities and attribution from the CAMA data for all but one municipality. These data were gathered from the CT municipalities by the COGs and then submitted to CT OPM. This dataset was created on September 2025 from data collected in 2024-2025. Data was processed using Python scripts and ArcGIS Pro for standardization and integration of the data. To learn more about Parcel and CAMA in CT visit our Parcels Page in the Geodata Portal.

Coordinate system:

This dataset is provided in NAD 83 Connecticut State Plane (2011) (EPSG 2234) projection as it was for 2024. Prior versions were provided at WGS 1984 Web Mercator Auxiliary Sphere (EPSG 3857).

Ownership Suppression:

The updated dataset includes parcel data for all towns across the state, with some towns featuring fully suppressed ownership information. In these instances, the owner’s name was replaced with the label "Current Owner," the co-owner’s name will be listed as "Current Co-Owner," and the mailing address will appear as the property address itself. For towns with fully suppressed ownership data, please note that no "Suppression" field was included in the submission to confirm these details and this labeling approach was implemented as the solution.

New Data Fields:

The new dataset introduces the “Property Zip” and “Mailing Zip” fields, which will display the zip codes for the owner and property.

Service URL:

In 2024, we implemented a stable URL to maintain public access to the most up-to-date data layer. Users are strongly encouraged to transition to the new service as soon as possible to ensure uninterrupted workflows. This URL will remain persistent, providing long-term stability for your applications and integrations. Once you’ve transitioned to the new service, no further URL changes will be necessary.

CAMA Notes:

The CAMA underwent several steps to standardize and consolidate the information. Python scripts were used to concatenate fields and create a unique identifier for each entry. The resulting dataset contains 1,354,720 entries and information on property assessments and other relevant attributes.

• CAMA was provided by the towns.

Spatial Data Notes:

Data processing involved merging the parcels from different municipalities using ArcGIS Pro and Python. The resulting dataset contains 1,282,833 parcels.

• No alteration has been made to the spatial geometry of the data.

• Fields that are associated with CAMA data were provided by towns.

• The data fields that have information from the CAMA were sourced from the towns’ CAMA data.

• If no field for the parcels was provided for linking back to the CAMA by the town a new field within the original data was selected if it had a match rate above 50%, that joined back to the CAMA.

• Linking fields were renamed to "Link".

• All linking fields had a census town code added to the beginning of the value to create a unique identifier per town.

• Any field that was not town name, Location, Editor, Edit Date, or a field associated back to the CAMA, was not used in the creation of this Dataset.

• Only the fields related to town name, location, editor, edit date, and link fields associated with the towns’ CAMA were included in the creation of this dataset. Any other field provided in the original data was deleted or not used.

• Field names for town (Muni, Municipality) were renamed to "Town Name".

Attributes included in the data:

• Town Name

• Owner

• Co-Owner

• Link

• Editor

• Edit Date

• Collection year – year the parcels were submitted

• Location

• Property Zip

• Mailing Address

• Mailing City

• Mailing State

• Mailing Zip

• Assessed Total

• Assessed Land

• Assessed Building

• Pre-Year Assessed Total

• Appraised Land

• Appraised Building

• Appraised Outbuilding

• Condition

• Model

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Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

Tempe’s roadways are an important means of transportation for residents, the workforce, students, and visitors. Tempe measures the quality and condition of its roadways using a Pavement Quality Index (PQI). This measure, rated from a low of 0 to a high of 100, is used by the City to plan for maintenance and repairs, and to allocate resources in the most efficient way possible.

This measure is created using pavement quality data maintained in the RoadMatrix Pavement Management Program. About every three years, the City surveys pavement, such as the smoothness of roadways and any signs of distress in the pavement surface. This data is then used to calculate the PQI, which determines roadway maintenance prioritization schedules as well as the most optimal road treatment options (such as placing a filler material in the cracks and treating the entire pavement surface, milling and replacing the top layer of the asphalt pavement, reconstructing the street section)

This page provides data for the performance measure related to PQI. To access geospatial data regarding PQI please visit https://data.tempe.gov/dataset/pavement-quality-index-segments

The performance measure dashboard is available at 1.22 Pavement Quality Index

This resource represents annual citywide average PQI.

This resource is used in the indicators found in the Safe and Secure Communities dashboard.

Additional Information

Source: Stantec/Road Matrix

Contact (author): Isaac Chavira

Contact E-Mail (author): isaac_chavira@tempe.gov

Contact (maintainer): Sue Taaffe

Contact E-Mail (maintainer): sue_taaffe@tempe.gov

Data Source Type: CSV

Preparation Method: Extracted from Roadmatrix and joined to GIS network

Publish Frequency: Annual (Average PQI)/Quarterly (Segment PQI)

Publish Method: Manual

Data Dictionary

Access control refers to the ability for vehicles to enter and exit a roadway. Full access control indicates limited access to a roadway, such as through ramps. Partial access control are roads with at-grade intersections but limited to no access directly from private driveways, such as the US 60 between Phoenix and Wickenburg. No access control indicates full access to the roadway, such as directly from private driveways. Refer to the HPMS Field Manual, Item 5 "Access Control" for more details and examples.

Reported Extent: All principle arterials (functional system 1-3) and sample panel sections. Sample panel sections are any roadways, or parts of roadways, that were identified by the Federal Highway Administration (FHWA) as requiring additional data to be reported in the 2023 HPMS report. Values may be available for other roads but are not actively maintained.

Key Terms:
ADOT, MPD, 2023 HPMS, 2023, HPMS