Used to map the shared City boundaries with Henrico County to the north and Chesterfield County to the south. All thematic GIS areas are to adhere to this commonly used boundary.Edited by GIS professionals in the city's Assessor's Office.

An ArcGIS OnLine map of National Historic Districts and National Historic Sites. Districts that are listed on the National Register of Historic Places (National Historic designation) and the Virginia Landmarks Register (State designation). Districts may logically carry either both designations or the State designation alone. Both of these programs are administered by the Virginia Department of Historic Resources in conjunction with the City of Richmond Bureau of Housing and Neighborhood Preservation. Properties that fall within these districts may be entitled to various development incentives. Note that some districts overlap.

This is the parcel lines used to build the parcel polygons.The shape length is the distance of the drawn line.The Legal Length is the distance of the known length from a deed or plat.Often these two do not agree because this GIS mapping is not legal surveys but mapping for tax assessments.

GRTC Transit System services the City of Richmond, Henrico County, parts of Chesterfield County, and provides express service to Petersburg.

Blocks are used to support mapping at smaller scales (1:10,000+), in which it is not practical to map individual parcels, but displaying the city blocks is useful for cartographic representation of land.Represents city blocks or polygon areas defined by the circumference of contiguous parcels. With respect to Office of the Assessor "keycards," blocks represent the first 8 digits of a keycard, i.e. parcels N0180478001, N0180478002, and N0180478003 would all be located on block N0180478.

ParcelBlockWetlandFlood PlainContour

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: heat indexUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Related layers include Morning Heat Index and Afternoon Heat Index. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, ORCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Evening Heat Index Observations in Floating-Point (°F)

THIS MAP SHOWS THE FOLLOWING:Designated Highways on the state-maintained system. The National Network and Virginia Qualifying Highways include all interstates and certain other highways respectively, with one road-mile of access permissible off these routes to access terminals, fuel, food, rest, and repairs (except in cities and towns, or in Henrico and Arlington Counties where permission is required from those localities). The Virginia Access portion includes certain other primary and secondary routes where no access is allowed off the system. LIMITATIONS & EXCLUSIONS REGARDING TRUCK ROUTES & RESTRICTIONS PROVIDED ON THIS MAP1. The information presented applies only to the highway systems maintained by the Virginia Department of Transportation (VDOT). Additional STAA access or restrictions may apply in incorporated cities and towns, and on secondary roads (numbered 600 or higher) in Henrico and Arlington Counties.2. Access to, or restrictions on, the indicated routes for the specified vehicles is applicable unless other restrictions or limitations apply due to weight, height, or width; incidents or construction; or as otherwise indicated by signs on the highway.3. To the best of our knowledge the information provided is updated on a weekly basis or as received from Districts. It is the responsibility of the highway user to ensure their information is current and correct.A permit for additional access off the state maintained system may be requested by contacting the Department of Motor Vehicles at (804) 497-7135. For additional access in cities and towns and in Henrico and Arlington Counties contact officials in those localities.

base_StreamLine originated from DPU's stm_Stream feature class. A copy of it was placed into the Basemap dataset for the purpose of supporting basic base map map service(s). The data is not dynamic, as streams do not change. Base mapping sources can be more isolated and not be dependent upon or interfer with DPU stormwater activities.base_StreamLine was also cleaned-up of extra fields established by DPU that serve no purpose for supporting base mapping: LifeCycleStatus, ProjectNumber, ProjectName,Accessible, FromAssetID, ToAssetID, Enabled, StormwaterTraceWeight, WaterwayImpairment, TMDLPresent, MultipleTMDLs, and RiskConditionScore.

A dataset listing Virginia cities by population for 2024.

This preliminary experimental lithogeochemical map shows the distribution of rock types in the Virginia and Maryland parts of the Chesapeake Bay watershed. The map was produced digitally by classifying geologic-map units according to composition, mineralogy, and texture; rather than by age and stratigraphic relationships as shown on traditional geologic maps. This map differs from most lithologic maps in that the lithogeochemical unit classification distinguishes those rock units having key water-reactive minerals that may induce acid neutralization, or reduction, of hosted water at the weathering interface. The validity of these rock units, however, is independent of water chemistry, because the rock units are derived from geologic maps and rock descriptions. Areas of high soil carbon content, and sulfide metal deposits are also shown. Water-reactive minerals and their weathering reactions yield five lithogeochemical unit classes: 1) carbonate rock and calcareous rocks and sediments, the most acid-neutralizing; 2)carbonaceous-sulfidic rocks and sediments, oxygen-depleting and reducing; 3) quartzofeldspathic rocks and siliciclastic sediments, relatively weakly reactive with water; 4) mafic silicate rocks/sediments, oxygen consuming and high solute-load delivering; and, 5) the rarer calcareous-sulfidic (carbonaceous) rocks, neutralizing and reducing. Earlier studies in some parts of the map area have related solute loads in ground and stream waters to some aspects of bedrock lithology. More recent preliminary tests of relationships between four of the classes of mapped lithogeochemical units and ground water chemistry, in the Mid-Atlantic area using this map, have focused on and verified the nitrate-reducing and acid-neutralizing properties of some bedrock and unconsolidated aquifer rock types. Sulfide mineral deposits and their mine-tailings effects on waters are beginning to be studied by others. Additional testing of relationships among the lithogeochemical units and aspects of ground and surface water chemistry could help to refine the lithogeochemical classification, and this map. The testing could also improve the usefulness of the map for assessing aquifer reactivity and the transport properties of reactive contaminants such as acid rain, and nitrate from agricultural sources, in the Chesapeake Bay watershed.

Public Demonstration Maps Demographics 2.23.2022

This web map represents the time-line of City of Richmond annexed areas. From its origination in 1742 to 1970.

This map is NOT a City Council proposed redistricting plan. It is a result of a public mapping demonstration. Ideas in this map could be used by Richmond City Council as contributing toward future proposed plan(s). The purpose of sharing it is for the public to review and comment upon the ideas observed in the map.

base_StreamPolygon originated from DPU's stm_StreamShape feature class. A copy of it was placed into the Basemap dataset for the purpose of supporting basic base map map service(s). The data is not dynamic, as streams do not change. Base mapping sources can be more isolated and not be dependent upon or interfer with DPU stormwater activities.base_StreamPolygon was also cleaned-up of extra fields established by DPU that serve no purpose for supporting base mapping: LifeCycleStatus, InstallDate, InstallContractor, ConstructionInspector, WorkOrderNumber, OracleInquiryNumber, DPUDrawingNumber, ProjectNumber, and ProjectName,

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The file is georeferenced to earth's surface using the UTM projection and coordinate system. The specifications for the horizontal control of DFIRM data files are consistent with those required for mapping at a scale of 1:12000.

The City’s GIS reflects the DFIRM maps from 2009 and the 2013 revision. In 2013, there was a revision to the DFIRM maps, which mainly affected the Reedy Creek corridor and an area downtown. The City GIS floodplains depict both the original 2009 DFIRM information and where FEMA specified, the DFIRM 2013 revision areas were updated. 500 Year Floodplain areas are used in the City's permitting processes, as Federal regulations and local floodplain management ordinances can effect development activities in these areas. These GIS layers could be used to flag properties (hence addresses) as to whether or not they are in a 500 Year Floodplain for use in the City's data warehousing and central address systems; currently these layers are not used in the GIS summary process.

NB: This layer will no longer be the authoritative SFHA on July 8, 2025. Starting July 8, the new SFHA is here.The City’s GIS reflects the DFIRM maps from 2009 and the 2013 revision. In 2013, there was a revision to the DFIRM maps, which mainly affected the Reedy Creek corridor and an area downtown. The City GIS floodplains depict both the original 2009 DFIRM information and where FEMA specified, the DFIRM 2013 revision areas were updated. 100 Year Floodplain areas are used in the City's permitting processes, as Federal regulations and local floodplain management ordinances can effect development activities in these areas.

base_LakePolygon originated from DPU's stm_LakeShape feature class. A copy of it was placed into the Basemap dataset for the purpose of supporting basic base map map service(s). The data is not dynamic, as lakes do not change. Base mapping sources can be more isolated and not be dependent upon or interfer with DPU stormwater activities.base_LakePolygon was also cleaned-up of extra fields established by DPU that serve no purpose for supporting base mapping: LifeCycleStatus, InstallDate, InstallContractor, ConstructionInspector, WorkOrderNumber, OracleInquiryNumber, DPUDrawingNumber, ProjectNumber, and ProjectName.

The emergence of urban heat as a climate-induced health stressor is receiving increasing attention among researchers, practitioners, and climate educators. However, the measurement of urban heat poses several challenges with current methods leveraging either ground based, in situ observations, or satellite-derived surface temperatures estimated from land use emissivity. While both techniques contain inherent advantages and biases to predicting temperatures, their integration may offer an opportunity to improve the spatial resolution and global application of urban heat measurements. Using a combination of ground-based measurements, machine learning techniques, and spatial analysis, we addressed three research questions: (1) How much do ambient temperatures vary across time and space in a metropolitan region? (2) To what extent can the integration of ground-based measurements and satellite imagery help to predict temperatures? (3) What landscape features consistently amplify and temper heat? We applied our analysis to the city of Richmond, Virginia, using geocomputational machine learning processes on data collected on days when maximum air temperatures were above the 90th percentile of historic averages. Our results suggest that the urban microclimate was highly variable—with differences of up to 10 C between coolest and warmest locations at the same time—and that these air temperatures were primarily dependent on underlying landscape features. Additionally, we found that integrating satellite data with ground-based measures provided highly accurate and precise descriptions of temperatures in all three study regions. These results suggest that accurately identifying areas of extreme urban heat hazards for any region is possible through integrating ground-based temperature and satellite data.