Link to AMAFCA site with stormwater maps and data features are available, including shapefiles and interactive maps.

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: Sensing package time step valuesUnits: decimal degrees 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.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Albuquerque NMAfternoon air temperatures in cities

Albuquerque, NM 2016 crimes. Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis). Data obtained from the Albuquerque Police Department (see ABQ Data). Note: Composite of all crime types reported by APD.

Geospatial data about City of Albuquerque, New Mexico Major Bike Paths. Export to CAD, GIS, PDF, CSV and access via API.

Parcels maintained by the City of Albuquerque. These are different from the Bernalillo County Parcels.

Data release to accompany the SIR: Modeling potential water-table elevation change as a result of projected pumping scenarios near Kirtland Air Force Base in Albuquerque, New Mexico. Data release includes the modified MNW2 files for the local-scale and regional model of the MODFLOW LGR-2 version of the updated Middle Rio Grande regional model, published here: https://water.usgs.gov/GIS/metadata/usgswrd/XML/sir2019-5052.xml. The pumping scenarios used to modify the MNW2 files are based on proposed pumping scenarios published by the Albuquerque Bernalillo County Water Utility Authority (ABCWUA, 2016).

Land use for areas within the City of Albuquerque. The land use codes were converted to a new set of codes in March 2019 in coordination between the City of Albuquerque Planning Department Urban Design and Development and AGIS Divisions and staff at Mid-Region Council of Governments. The Land Use Category field is a generalized Category that follows more closely what Mid-Region Council of Government staff use for their land use modeling activities. The Land Use Description field follows more closely the types of land uses outlined in the Integrated Development Ordinance (IDO) that became effective May 17, 2018. These Use Regulations are listed in Part 14-16-4 within the document. The Old Land Use Category field is how the Land Use layer was generalized prior to March 2019 (starting from the mid-1990s) and the Old Land Use Description field includes the codes that were in use prior to March 2019 (starting from the mid-1990s). These codes will remain in place until it is determined by AGIS that they are no longer needed. Updates to the land uses are ongoing, including an effort starting March 2019 from direct feedback supplied by Urban Design and Development staff.

GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy and planning for provision of public services. However, uncertainties in the road datasets used to calculate measures of spatial access remain largely underreported. These uncertainties might result in biases within decision-making that strives for social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance resulting from street layer uncertainty (e.g. proprietary, free, and volunteer-information-based streets) and its propagation in a multi-modal enhanced 2-step floating catchment area (MM-E2SFCA) model of spatial accessibility for car and bus transportation, using datasets in the metropolitan area of Albuquerque, NM, USA. We proposed and demonstrated a novel approach as a solution – the spatial access ratio (SPAR). Results indicate that travel impedance disagreement among different street sources propagate through the modeling process to effect Spatial Access Index (SPAI) estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. SPAR reduced uncertainties introduced by the choice of model parameter or street datasets, providing a suitable alternative to SPAI for analyses that do not require an absolute measure of supply to demand ratio. Careful selection of street source data and consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy.

description: This data has been collected by the U.S. Bureau of Land Management (BLM) in New Mexico at the New Mexico State Office. The initial data source is the statewide Public Land Survey System (PLSS) coverage for the state of New Mexico, generated at the BLM New Mexico State Office. Additional data was onscreen-digitized from BLM Cadastral Survey Plats and Master Title Plats, or tablet-digitized from 1:24,000 paper maps. This revision reflects boundary adjustments made in the Albuquerque area to more accurately reflect boundaries as depicted on USGS 1:24,000 topographic maps. Note for Shapefiles: Shapefiles have been created from coverages using the "Export Coverage to Shapefile" function in ArcGIS 8.3. All occurrences of "#" and "-" in INFO item names are replaced with an underscore character. This includes COVER# and COVER-ID, which become "COVER_" and "COVER_ID". Additionally, the Shapefile format only allows 10 characters in item names, so long item names are truncated. To avoid duplicate names, the items are truncated and assigned consecutive numbers. For example, in a coverage called CITY_STREET the items "CITY_STREET#" and "CITY_STREET-ID" become "CITY_STRE" and "CITY_STR_1" .; abstract: This data has been collected by the U.S. Bureau of Land Management (BLM) in New Mexico at the New Mexico State Office. The initial data source is the statewide Public Land Survey System (PLSS) coverage for the state of New Mexico, generated at the BLM New Mexico State Office. Additional data was onscreen-digitized from BLM Cadastral Survey Plats and Master Title Plats, or tablet-digitized from 1:24,000 paper maps. This revision reflects boundary adjustments made in the Albuquerque area to more accurately reflect boundaries as depicted on USGS 1:24,000 topographic maps. Note for Shapefiles: Shapefiles have been created from coverages using the "Export Coverage to Shapefile" function in ArcGIS 8.3. All occurrences of "#" and "-" in INFO item names are replaced with an underscore character. This includes COVER# and COVER-ID, which become "COVER_" and "COVER_ID". Additionally, the Shapefile format only allows 10 characters in item names, so long item names are truncated. To avoid duplicate names, the items are truncated and assigned consecutive numbers. For example, in a coverage called CITY_STREET the items "CITY_STREET#" and "CITY_STREET-ID" become "CITY_STRE" and "CITY_STR_1" .

Albuquerque and Bernalillo County estimated childhood obesity 2010. Original data obtained from the CDC. An example using ArcGIS Optimized Hot Spot Analysis (see http://www.unm.edu/~lspear/other_nm.html for more information).

The county water level monitoring program was formally launched in February 2010. Based on data collected since that time, the Office of the State Engineer closed the Sandia Underground Water Basin to new water rights appropriations, though new and replacement domestic wells can still be drilled. The data indicate that some area of the county, particularly the East Mountains, are experiencing significant annual water level declines on the order of two feet per year or greater. The East Mountains and North Albuquerque Acres areas are heavily dependent on domestic wells or water supply.

Groundwater Dashboard: https://ipgr.maps.arcgis.com/apps/dashboards/feade3c967a648a4a92b2ab05eee46df

Information on the program and a participation application are available at https://www.bernco.gov/public-works/public-works-services/water-wastewater-stormwater/groundwater-resources/groundwater-projects.

Abstract: Monthly and annual average solar resource potential for Hawaii.

Purpose: Provide information on the solar resource potential for Hawaii. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in watt hours.

Other Citation Details:

George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM.

License Info

DISCLAIMER NOTICE This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Albuquerque Police Department Area CommandsMetadata

Public wifi access points provided by the City of Albuquerque for free.

Address points are placed at the centroid of City parcels. They may not match Bernalillo County Parcels.Metadata

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: Sensing package time step valuesUnits: decimal degrees 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.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Albuquerque NMAfternoon air temperatures in cities

GIS data layer that spatially define land use and zoning regulations detailed in the City of Albuquerque Integrated Development Ordinance (IDO) that was adopted in November 2017 with amendments in April and May 2018. The IDO became effective on May 17, 2018.

USNG is standard that established a nationally consistent grid reference system. It provides a seamless plane coordinate system across jurisdictional boundaries and map scales; it enables precise position referencing with GPS, web map portals, and hardcopy maps. USNG enables a practical system of geoaddresses and a universal map index. This data resides in the GCS 1983 coordinate system and is most suitable for viewing over North America.

This layer shows 100 meter grid squares for the Albuquerque NM metropolitan area.

Rank III (3) plans governing the development of the Albuquerque / Bernalillo County area in accordance with the Rank I Comprehensive Plan.

Discover the latest resources, maps and information about the coronavirus (COVID-19) outbreak in Albuquerque, NM