Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Project Atlas - São Paulo

Project Atlas - São Paulo is a Data Science and Engineering initiative that aims at developing relevant and curated Geospatial features about the city of São Paulo, Brazil. It's ultimate use is varied, but it is mainly focused on Machine Learning tasks, such as Real State price prediction.

It aggregates several attributes from many public data sources at different levels of interest, which can be used to match geospatially referenced data (lat,long pairs for example).

A breakdown of the data sources currently used and their original references can be found below, but the official documentation of the project contains the full list of data sources.

Data sources

• GeoSampa: geospatial data exploration tool provided by the São Paulo's Department of Urban Development;
• IBGE: raw datasets from the 2010 Census conducted by the Brazilian Institute of Geography and Statistics;
• Infocidade: multiple sources from the city of São Paulo's local government entities;
• São Paulo Open Data Portal: multiple curated data sources from the city of São Paulo;

Relevant files:

1. tb_district.parquet: the dataset with all derived features aggregated at the District level;
2. tb_neighborhood.parquet: the dataset with all derived features aggregated at the Neighborhood level;
3. tb_zipcode.parquet: the dataset with all derived features aggregated at the Zipcode level;
4. tb_area_of_ponderation: the dataset with all derived features aggregated at the Area of Ponderation level;

Inspiration

This project had various inspirations, such as the Boston Housing Dataset. While I was studying relevant features for the real state market, I noticed that the classic Boston Housing dataset included several sociodemographic variables, which gave me the idea to do the same for São Paulo using the Brazilian Census data.

Acknowledgements

Photo by Lucas Marcomini on Unsplash

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

This data release includes GIS datasets supporting the Colorado Legacy Mine Lands Watershed Delineation and Scoring tool (WaDeS), a web mapping application available at https://geonarrative.usgs.gov/colmlwades/. Water chemistry data were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS), U.S. Environmental Protection Agency (EPA) STORET database, and the USGS Central Colorado Assessment Project (CCAP) (Church and others, 2009). The CCAP study area was used for this application. Samples were summarized at each monitoring station and hardness-dependent chronic and acute toxicity thresholds for aquatic life protections under Colorado Regulation No. 31 (CDPHE, 5 CCR 1002-31) for cadmium, copper, lead, and/or zinc were calculated. Samples were scored according to how metal concentrations compared with acute and chronic toxicity thresholds. The results were used in combination with remote sensing derived hydrothermal alteration (Rockwell and Bonham, 2017) and mine-related features (Horton and San Juan, 2016) to identify potential mine remediation sites within the headwaters of the central Colorado mineral belt. Headwaters were defined by watersheds delineated from a 10-meter digital elevation dataset (DEM), ranging in 5-35 square kilometers in size. Python and R scripts used to derive these products are included with this data release as documentation of the processing steps and to enable users to adapt the methods for their own applications. References Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, B.W., McEachron, Luke, and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado: U.S. Geological Survey Data Series 614, 76 p., https://doi.org/10.3133/ds614. Colorado Department of Public Health and Environment (CDPHE), Water Quality Control Commission 5 CCR 1002-31. Regulation No. 31 The Basic Standards and Methodologies for Surface Water. Effective 12/31/2021, accessed on July 28, 2023 at https://cdphe.colorado.gov/water-quality-control-commission-regulations. Horton, J.D., and San Juan, C.A., 2022, Prospect- and mine-related features from U.S. Geological Survey 7.5- and 15-minute topographic quadrangle maps of the United States (ver. 8.0, September 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F78W3CHG. Rockwell, B.W. and Bonham, L.C., 2017, Digital maps of hydrothermal alteration type, key mineral groups, and green vegetation of the western United States derived from automated analysis of ASTER satellite data: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5RK7.

Managing 245 million acres of land and 700 million acres of mineral estate is a big task. The BLM recognizes that geospatial information is a critical tool for managing public lands. We’ve already made great strides in creating national datasets, supporting almost every program in the Bureau. The BLM has adopted a ground-up approach to managing public lands, and the geospatial program is providing the structure and tools to accomplish this strategy. We manage spatial data to support multiple activities at varying scales.

The BLM's geospatial strategy focuses on collection, organization, and use of baseline resource management data, like fenceline and transportation data and enhancing predictions based on geospatial data. Examples of activities that require geospatial data include planning and resource management, special status species monitoring, regional mitigation, and renewable energy projects, just to name a few.

An important factor in implementing our strategy is using a geographic information system (GIS) that is consistent and integrated within the Bureau and the Department of the Interior. This internal cohesion enhances the BLM's ability to partner with other Federal agencies, collaborate with State and Tribal governments, and communicate with the public.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

Geospatial Data Platform Market Outlook

According to our latest research, the global geospatial data platform market size reached USD 108.5 billion in 2024, demonstrating robust expansion driven by digital transformation and increasing demand for location-based analytics. The market is projected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 341.2 billion by 2033. This remarkable growth is attributed to the rising integration of geospatial technologies across sectors such as urban planning, disaster management, transportation, and agriculture, alongside ongoing advancements in cloud computing and artificial intelligence that are reshaping how spatial data is collected, processed, and utilized.

One of the primary growth factors fueling the geospatial data platform market is the escalating adoption of smart city initiatives globally. Urbanization has compelled governments and municipalities to seek innovative solutions for infrastructure management, resource allocation, and public safety, all of which heavily rely on real-time geospatial data. The proliferation of Internet of Things (IoT) devices and sensors has further enriched the data ecosystem, enabling more granular and actionable insights. As cities become more connected and data-driven, the need for robust geospatial platforms that can aggregate, analyze, and visualize complex datasets is becoming indispensable, driving both public and private sector investments in this technology.

Another significant driver is the increasing frequency and intensity of natural disasters, which has heightened the reliance on geospatial data platforms for disaster management and mitigation. Accurate geospatial intelligence is critical for early warning systems, emergency response planning, and post-disaster recovery. Governments, humanitarian agencies, and insurance companies are leveraging these platforms to enhance situational awareness, optimize resource deployment, and minimize losses. The integration of satellite imagery, drone data, and advanced analytics within geospatial platforms enables rapid assessment of affected areas, improving the efficacy of relief operations and long-term resilience planning.

The expansion of the geospatial data platform market is also being propelled by the transformation of industries such as agriculture, utilities, and transportation. Precision agriculture, for example, utilizes spatial data to optimize crop yields, monitor soil health, and manage water resources efficiently. Utilities are adopting geospatial solutions for asset management, outage tracking, and network optimization, while the transportation and logistics sector is leveraging these platforms for route planning, fleet management, and supply chain visibility. The convergence of artificial intelligence, machine learning, and big data analytics with geospatial data platforms is unlocking new levels of operational efficiency and strategic decision-making across these industries.

From a regional perspective, North America continues to dominate the geospatial data platform market due to its advanced technological infrastructure, strong presence of leading market players, and substantial government investments in geospatial intelligence. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding infrastructure projects, and increasing adoption of geospatial technologies in emerging economies such as China and India. Europe remains a significant market, supported by regulatory mandates for spatial data sharing and the emphasis on sustainability and environmental monitoring. Latin America and the Middle East & Africa are also experiencing steady growth, albeit from a smaller base, as digital transformation initiatives gain momentum across diverse sectors.

The emergence of the Spatial Computing Platform is revolutionizing how geospatial data is processed and utilized. This platform integrates spatial computing with geospatial technologies, enabling more immersive and interactive data visualization. By leveraging augmented reality (AR) and virtual reality (VR), spatial computing platforms allow users to experience geospatial data in three dimensions, providing a deeper understanding of spatial relationships and patterns. This innovation is particularly beneficial in fields such as urban plannin

River Surface Reflectance Database

A Geospatial Resource

By [source]

About this dataset

This dataset provides an invaluable collection of surface reflectance values across all rivers in the contiguous USA which are ~60 meters wide or more. The set contains level 1 collection 1 data for Landsat 5, 7 and 8 and is geo-referenced to river centerlines with network topology (NHDPlusV2). As a result, users can quickly conduct detailed geospatial analysis with this comprehensive dataset. For example, each record contains the NHDPlusV2 centerline ID as well as various surface reflectance values such as those of red, green, blue and near infrared bands. These bands present the median reflectance of pixels detected within each Landsat scene that lie inside each reach's boundaries. This information helps us better understand how different components of aquatic ecosystems vary across space - aiding research activities such as habitat assessment or species migration studies like never before!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

• Understand Data: Once you clicked on it, you will get an overview of all information regarding this dataset like number of observations, original source for the data, types of columns etc.. You should also read through columns descriptions to better understand what each column contains.

• Exploratory Analysis & Visualization: After understanding what data is present in this dataset it's a good idea to do some exploratory analysis and visualization using graphical tools such as pandas for Python or Tableau for example. This will help you see patterns or trends which are otherwise difficult to identify at first glance when looking at raw numbers or text description alone alone. While visualizing focus specifically on those variables which have maximum impact on overall performance i-e reachids, latitudes and longitudes of rivers center lines etcetera as they are more likely to contain more useful insights than rest!

• Modeling & Prediction: Now that you know basic information regarding your dataset, try and build different regression models like linear regression (for predicting lengths), Time Series (predicting band values) or any other model depending upon your requirements to gain further insight into our datasets so decisions can be quickly taken based upon factual evidence!

5 Reachid_ID & COMID_ID Files : In addition , there are two files namely Reachid_ID & COMID ID in this dataset , containing river COMIDs (validation numbers) combined with their corresponding IDs . These files enable users quickly identify what particular center line correspondes with which reach easily without having manually go through hundreds if not thousands records!

Research Ideas

• Using the median reflectance values of the different bands, it could be used to identify areas with high or low chlorophyll concentrations in a river, which can give an indication of the water quality and presence of aquatic life.
• Comparing surface reflectance across different rivers over time could help identify changes in land use that have impacted the adjacent tributary system’s condition over time.
• By analyzing the surface reflectance levels for specific versions, ecological assessments could be performed on a river to determine its health and potential management strategies needed for protecting against human threats such as pollutants, sedimentation etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: COMID_ID.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The Public Water Supply (PWS) datalayer contains the locations of public community surface and groundwater supply sources and public non-community supply sources as defined in 310 CMR 22.00. The public water supply systems represented in this datalayer are based primarily on information in the DEPs Water Quality Testing System (WQTS) database. The WQTS database is the Department?s central database for tracking water supply data. The PWS datalayer also contains the locations of proposed wells that have a defined DEP approved wellhead protection area (Zone IIs). Proposed sources are not currently tracked in WQTS. In ArcSDE the layer is named PWSDEP_PT. As stated in 310 CMR 22.02, a Public Water System means a system for the provision to the public of piped water for human consumption if such system has at least 15 service connections or regularly serves an average of at least 25 individuals daily at least 60 days of the year. Such term includes (1) any collection, treatment, storage and distribution facilities under control of the operator of such a system and used primarily in connection with such system, and (2) any collection or pretreatment storage facilities not under such control which are used primarily in connection with such system. A public water system is either a community or a non-community water system. (a) Community water system means a public water system which serves at least 15 service connections used by year-round residents or regularly serves at least 25 year-round residents. (b) Non-community water system means a public water system that is not a community water system. 1. Non-transient non-community water system (NTNC) means a public water systems that is not a community water system and that regularly serves at least 25 of the same persons or more approximately four or more days per week, more that six months or 180 days per year, such as a workplace providing water to it?s employees. 2. Transient non-community water system (TNC) means a public water system that is not a community water system or a non-transient non-community water system but is a public water system which serves water to 25 different persons at least 60 days of the year. Some examples of these types of systems are: restaurants, motels, camp grounds, parks, golf courses, ski areas and community centers.

This abstract contains links to public ArcGIS maps that include locations of carbonate springs and some of their characteristics. Information for accessing and navigating through the maps are included in a PowerPoint presentation IN THE FILE UPLOAD SECTION BELOW. Three separate data sets are included in the maps:

1. Geochemistry data from the US Water Quality Portal (WQP), which compiles geochemistry data from the USGS and other federal agencies.
2. Discharge data from WoKaS, a world wide spring discharge data set (Olarinoye et al., 2020).
3. Regional karst data from selected US state agencies.

Several base maps are included in the links. The US carbonate map describes and categorizes carbonates (e.g., depth from surface, overlying geology/ice, climate). The carbonate springs map categorizes springs as being urban, specifically within 1000 ft of a road, or rural. The basis for this categorization was that the heat island effect defines urban as within a 1000 ft of a road. There are other methods for defining urban versus rural to consider. Map links and details of the information they contain are listed below.

Map set 1: The WQP map provides three mapping options separated by the parameters available at each spring site. These maps summarize discrete water quality samples, but not data logger availability. Information at each spring provides links for where users can explore further data.

Option 1: WQP data with urban and rural springs labeled, with highlight of springs with or without NWIS data https://www.arcgis.com/home/item.html?id=2ce914ec01f14c20b58146f5d9702d8a

Options 2: WQP data by major ions and a few other solutes https://www.arcgis.com/home/item.html?id=5a114d2ce24c473ca07ef9625cd834b8

Option 3:WQP data by various carbon species https://www.arcgis.com/home/item.html?id=ae406f1bdcd14f78881905c5e0915b96

Map 2: The worldwide carbonate map in the WoKaS data set (citation below) includes a description of carbonate purity and distribution of urban and rural springs, for which discharge data are available: https://www.arcgis.com/apps/mapviewer/index.html?webmap=5ab43fdb2b784acf8bef85b61d0ebcbe.

Reference: Olarinoye, T., Gleeson, T., Marx, V., Seeger, S., Adinehvand, R., Allocca, V., Andreo, B., Apaéstegui, J., Apolit, C., Arfib, B. and Auler, A., 2020. Global karst springs hydrograph dataset for research and management of the world’s fastest-flowing groundwater. Scientific Data, 7(1), pp.1-9.

Map 3: Karst and spring data from selected states: This map includes sites that members of the RCN have suggested to our group.

https://uageos.maps.arcgis.com/apps/mapviewer/index.html?webmap=28ed22a14bb749e2b22ece82bf8a8177

This data set is incomplete (as of October 13, 2022 it includes Florida and Missouri). We are looking for more information. You can share data links to additional data by typing them into the hydroshare page created for our group. Then new sites will periodically be added to the map: https://www.hydroshare.org/resource/0cf10e9808fa4c5b9e6a7852323e6b11/

Acknowledgements: These maps were created by Michael Jones, University of Arkansas and Shishir Sarker, University of Kentucky with help from Laura Toran and Francesco Navarro, Temple University.

TIPS FOR NAVIGATING THE MAPS ARE IN THE POWERPOINT DOCUMENT IN THE FILE UPLOAD SECTION BELOW.

"Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"SCAG has developed and maintained its regional geospatial dataset of land use information at parcel-level—approximately five million parcels in the SCAG Region. The parcel-based land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. After the successful release of SCAG’s 2016 regional land use dataset for the development of the Connect SoCal (the 2020 RTP/SCS), SCAG has initiated a process to annually update its regional land use information at the parcel-level (the Annual Land Use Update). For the Annual Land Use Update process, SCAG collected county assessor’s tax roll records (including parcel polygons and property information) from county assessor’s offices, plus other reference layers including California Protected Areas Database (CPAD), California School Campus Database (CSCD), Farmland Mapping and Monitoring Program (FMMP)'s Important Farmland, U.S. Department of Defense's Military Installations, Ranges, and Training Areas (MIRTA) as well as SCAG's regional geospatial datasets, such as airport polygons and water body polygons.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.Data DescriptionFIELD_NAMEDESCRIPTIONPID202020 SCAG's unique parcel identifierAPN202020 Assessor Parcel NumberAPN20_P2020 Assessor Parcel Number - Parent Parcel (if applicable)COUNTYCounty nameCOUNTY_IDCounty FIPS codeCITYCity nameCITY_IDCity FIPS codeMULTIPARTMultipart feature (the number of multipart polygons; '1' = singlepart feature)STACKDuplicate geometry (the number of stacked polygons; '1' = no duplicate polygons)ACRESParcel area (in acres)SLOPESlope information1GEOID202020 Census Block GEOIDAPN_DUPDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIORatio of improvements assessed value to land assessed valueALU202020 Existing Land UseALU20_SRC2020 Existing Land Use Source2GP19_CITY2019 Jurisdiction’s general plan land use designationGP19_SCAG2019 SCAG general plan land use codeSP19_CITY2019 Jurisdiction’s specific plan land use designationSP19_SCAG2019 SCAG specific plan land use codeZN19_CITY2019 Jurisdiction’s zoning codeZN19_SCAG2019 SCAG zoning codeSP19_INDEX2019 Specific Plan Index ('0' = outside specific plan area; '1' = inside specific plan area)DC_BLTDecade built of existing structure (example: year built between 1960-1969 is '1960s')3BF_SQFT Building footprint area (in square feet)4PUB_OWNPublic-owned land index ('1' = owned by public agency)PUB_TYPEType of public agency5ADU_STATEThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)6, (1 = ADU eligible parcel, 0 = Not ADU eligible parcel)SF_UNBUILTDifference between parcel land area and building footprint area expressed in square feetFLOODParcel intersects with flood areas delineated by the Federal Emergency Management Agency (FEMA), obtained from the Digital Flood Insurance Rate Map from FEMA in August 2017. FIREParcel intersects with CalFire State Responsibility Areas Fire Hazard Severity zones (high and very high severity), dated 9/29/2023 and implemented 4/1/2024. WUIParcel intersects with Wildland-Urban Interface or Intermix zones, utilized from CAL FIRE’s Fire and Resource Assessment Program (FRAP), Wildland-Urban Interface (WUI) and Wildland-Urban Intermix (2020). See CAL FIRE for details. SEARISE36Parcel intersects with USGS Coastal Storm Modeling System (CoSMos) One-Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2, 2018)WETLANDParcel intersects a wetland or deepwater habitat, obtained from the US Fish and Wildlife Services National Wetlands Inventory Data (2020)HABITATParcel intersects with habitat connectivity corridors. Data is obtained from the California Department of Fish and Wildlife Habitat Essential Connectivity Project (2010).CONSERVParcel intersects with Areas of Conservation Emphasis (ACEIIv2), obtained from California Department of Fish and Wildlife Areas of Conservation Emphasis (2015)SOARParcel intersects with publicly owned open space identified by the County of Ventura Save Our Agricultural Resources (SOAR, 2017), which consist of a series of voter initiatives that require a majority vote of the people before agricultural land or open space areas can be rezoned for developmentCPADParcel intersects with publicly owned protected open space lands in the State of California through fee ownership as identified in the 2021 California Protected Areas Database (CPAD)CCEDParcel intersects with lands protected under conservation easements as identified in the 2021 California Conservation Easement Database (CCED)TRIBALParcel intersects with the tribal lands for the 16 Federally Recognized Tribal entities in the SCAG region, obtained from the American Indian Reservations/ Federally Recognized Tribal Entities dataset (2021)MILITARYParcel intersects with military lands managed by the US Department of Defense as of 2018FARMLANDParcel intersects with farmlands as identified in the Farmland Mapping and Monitoring Program (FMMP) in the Division of Land Resource Protection in the California Department of Conservation (2018)GRRA_INDEXThe number of Green Region Rresource Areas (GRRAs) that the parcel intersects with. GRRAs are areas where climate hazard zones, environmental sensitivities, and administrative areas where growth would generally not advance SB 375 objectives. See Connect SoCal 2024 Land Use & Communities Technical Report for details. UAZParcel centroid lies within Caltrans 2020 Adjusted Urbanized Area TCAC_2024The opportunity/resource level in the 2024 CTCAC/HCD Opportunity Map SB535_INDEXField takes a value of 1 if parcel intersects with SB 535 Disadvantaged Communities. See Connect SoCal 2024 Equity Analysis Technical Report for details. PEC_INDEXField takes a value of 1 if parcel's block falls within Priority Equity Communities. See Connect SoCal 2024 Equity Analysis Technical Report for details. PDA_INDEXThe number of Priority Development Areas (PDAs) that the parcel's largest overlapping area falls in. PDAs in Connect SoCal 2024 include Neighborhood Mobility Areas (NMAs), Transit Priority Areas (TPAs), Livable Corridors and Spheres of Influence (SOIs) (in unincorporated areas only). See Connect SoCal 2024 for details. PDA_NMAField takes a value of 1 if the parcel's largest overlapping area falls within Neighborhood Mobility Areas. See Connect SoCal 2024 for details. PDA_LCField takes a value of 1 if the parcel's largest overlapping area falls within Livable Corridors. See Connect SoCal 2024 for details. PDA_SOIField takes a value of 1 if the parcel's largest overlapping area falls within Spheres of Influence (SOIs) (in unincorporated areas only). See Connect SoCal 2024 for details. PDA_TPAField takes a value of 1 if the parcel's largest overlapping area falls within Transit Priority Areas. See Connect SoCal 2024 for details. APPAREL1MIThe number of apparel stores within a 1-mile drive7EDUC1MIThe number of educational institutions within a 1-mile drive7GROCERY1MIThe number of grocery stores within a 1-mile drive7HOSPIT1MIThe number of hospitals within a 1-mile drive7RESTAUR1MIThe number of restaurants within a 1-mile drive7JOBS_30MINThe number of the region's jobs accessible within a 30-minute commute by car during morning peak hour (6-9am) in 2050 based on Connect SoCal 2024 travel demand modeling. See Equity Technical Report for details. VMT_TOTAverage daily vehicle miles traveled (VMT) per average resident in the parcel’s transportation analysis zone (TAZ) in 2019, rounded to the nearest mile. This field contains results derived from Connect SoCal 2024’s activity-based travel demand model and do not reflect survey data, do not reflect VMT in any particular parcel within a TAZ, and are not validated at the TAZ-level. SCAG assumes no liability arising from the use of this data.8VMT_WORKAverage daily vehicle miles traveled (VMT) per average resident for work purposes in the parcel’s transportation analysis zone (TAZ) in 2019, rounded to the nearest mile. This field contains results derived from Connect SoCal 2024’s activity-based travel demand model and do not reflect survey data, do not reflect VMT in any particular parcel within a TAZ, and are not validated at the TAZ-level. SCAG assumes no liability arising from the use of this data.8JURIS_PLUSSub-jurisdictional geography in Los Angeles City (Community Plan Areas) and unincorporated areas of Los Angeles County (Planning Areas)YEARDataset YearShape_LengthLength of feature in internal unitsShape_AreaArea of feature in internal units squared1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. ASSESSOR- Assessor's 2020 tax roll records; CPAD- California Protected Areas Database (version 2020b; released in December 2020); CSCD- California School Campus Database (version 2021; released in March 2020); FMMP- Farmland Mapping and

Chicago sites that offer free or affordable technology resources and services, like computers with Internet access, Wi-Fi hotspots and technology training. Call or visit the organization's website before going to the location. For more information, visit http://locations.weconnectchicago.org/.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Geospatial Multimodal AI Platform Market Outlook

According to our latest research, the global Geospatial Multimodal AI Platform market size in 2024 stands at USD 3.8 billion, reflecting robust momentum across industries integrating AI-driven spatial analytics. The market is expected to reach USD 17.2 billion by 2033, progressing at a strong CAGR of 18.2% during the forecast period. This remarkable growth is primarily propelled by the surging demand for advanced geospatial intelligence, the proliferation of sensor-enabled IoT devices, and the convergence of multimodal data sources to power next-generation applications in urban planning, transportation, defense, and environmental monitoring.

The primary growth driver for the Geospatial Multimodal AI Platform market is the rapid technological advancement in artificial intelligence, particularly in machine learning and deep learning algorithms. These advancements are enabling platforms to process, analyze, and interpret vast volumes of geospatial data from multiple modalities—such as text, images, audio, video, and sensor data—delivering actionable insights with unprecedented accuracy and speed. This capability is especially valuable for smart city initiatives, where real-time analysis of multimodal data can optimize urban mobility, infrastructure management, and public safety. The integration of AI with geospatial analytics is thus transforming traditional GIS solutions into intelligent, predictive platforms that support data-driven decision-making across sectors.

Another significant factor fueling market expansion is the exponential growth of IoT devices and remote sensing technologies. The proliferation of sensors, drones, satellites, and connected devices is generating massive streams of spatial data, which, when combined with AI, unlock new possibilities for monitoring, forecasting, and automating complex processes. For example, in agriculture, multimodal AI platforms can synthesize satellite imagery, weather data, and sensor inputs to optimize crop yields and resource utilization. Similarly, in disaster management, these platforms enable real-time situational awareness by integrating video feeds, social media text, and sensor data, thereby enhancing emergency response and resilience.

Geospatial Analytics AI is becoming increasingly pivotal in the evolution of geospatial multimodal AI platforms. By leveraging advanced AI techniques, these platforms can process and analyze complex geospatial datasets with greater precision and speed. This capability is essential for industries that rely on real-time data interpretation, such as urban planning and disaster management. The integration of AI into geospatial analytics not only enhances data accuracy but also enables predictive modeling, which is crucial for proactive decision-making. As AI technologies continue to evolve, their application in geospatial analytics is expected to expand, offering new opportunities for innovation and efficiency across various sectors.

Furthermore, the increasing adoption of cloud-based deployment models is accelerating the accessibility and scalability of geospatial multimodal AI platforms. Cloud infrastructure allows organizations to process and store large datasets cost-effectively, while also facilitating collaborative analytics and integration with other enterprise systems. This trend is particularly evident among government agencies and large enterprises seeking to modernize their spatial intelligence capabilities without the constraints of on-premises hardware. Additionally, the growing emphasis on sustainability and environmental monitoring is driving demand for platforms that can analyze diverse data sources to track climate change, manage natural resources, and mitigate environmental risks.

From a regional perspective, North America currently leads the market, accounting for the largest share in 2024, driven by significant investments in smart infrastructure, defense modernization, and advanced research. However, Asia Pacific is emerging as the fastest-growing region, with governments and private sectors in countries like China, Japan, and India heavily investing in geospatial technologies for urbanization and disaster management. Europe is also witnessing substantial growth, fueled by initiatives in environmental monitoring and transportation. Overall, the

This layer is utilized in Next Generation 911 for both geospatial call routing and location validation functions. Creation and maintenance of data is performed by local Public Safety Answering Points in partnership with counties, GIS vendors, and other public safety agencies and with support from the Nebraska Public Service Commission. Disparate datasets are aggregated at the state level and provisioned to the NG911 core services by the 911 department of the PSC. The component datasets have been standardized sufficiently to serve the purposes of NG911 core services, but differences in methodology may persist depending on the use cases for local jurisdictions; for example, some counties may develop points to represent the points of actual structures, while others may develop points representing access points to properties. Similarly, some jurisdictions may have sub-address points for locations with multiple structures or units sharing an address, while others may only have a single point to represent such locations.

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structure ...

In 2014, BWSR received a grant from LCCMR (Legislative-Citizen Commission on Minnesota Resources) to produce a geospatial database template (i.e. with empty feature classes and tables) designed to contain Minnesota Statute 103E public drainage system data from local drainage authorities (e.g. Counties, Watershed Districts). The template is intended to help drainage authorities modernize and better manage their drainage system records. In addition, the template puts the data into a consistent, standardized form that makes is more readily accessible to users such as hydrologists and water managers.

However, as a result of a stipulation for receiving the grant, BWSR requires that those drainage authorities that use the template make their hydrographic data (ditch/tile centerlines, drainage structures, profile points and watershed boundaries) available annually to the public via the Geospatial Commons. Click the View button below to download the template package.

For guidance on how to use the template to modernize your drainage records please watch the DRM Template User Webinar. Also, for step-by-step instructions please see the recently updated Drainage Records Modernization Guidelines.

For more information on the project itself see the Drainage Records Modernization and GIS Database (DRMGD) Project section on https://bwsr.state.mn.us/drainage-minnesota. Also, CLICK HERE to see an interactive web map of example drainage records.

Unveiling Copenhagen's Public Backbone: A Comprehensive Geospatial Dataset of Municipal Units

This dataset offers an unparalleled glimpse into the organizational structure and geographical footprint of Københavns Kommune (Copenhagen Municipality), Denmark. As an essential open data initiative, it meticulously maps the locations of the city's vast network of public services, administrative offices, and community facilities.

Context and Source: The data originates directly from Copenhagen Municipality's official open data portal (KKORG), ensuring its authenticity and reliability. It is a live dataset, actively maintained and kept up-to-date by the municipality’s dedicated basic data controller. This continuous upkeep means users are accessing the most current information available on Copenhagen's public infrastructure. Unlike static datasets, this reflects the dynamic nature of urban development and service provision. The dataset provides a granular view, down to specific addresses and geographical coordinates, making it invaluable for precise location-based analysis.

What's Included: The dataset covers an extensive spectrum of municipal entities, categorized to provide a clear understanding of the city's public offerings:

• Employment & Social Welfare: Detailed locations of employment centers, job centers, departments handling benefit services, and crucial citizen-oriented support points for children and young people, individuals with disabilities, the socially vulnerable, and those with mental health challenges.
• Education & Childcare: Addresses of daycare centers and schools, fundamental to the city's educational and early childhood development landscape.
• Health Services: Comprehensive listings of dentists, specialized health centers (e.g., dedicated centers for cancer, child & youth health, diabetes management), and stress clinics, showcasing the municipality's commitment to public health.
• Cultural & Recreational Facilities: Geospatial data for sports grounds (including swimming pools), libraries, vibrant cultural centers, and museums, highlighting the city's efforts in fostering community well-being and cultural enrichment.
• Administrative & Support Units: Locations of area offices, property administration departments, centralized group services (including IT and property management), which form the operational backbone of the municipal government.

Inspiration and Potential Use Cases: This dataset is born from Copenhagen's commitment to transparency and empowering data-driven decisions. It serves as a cornerstone for:

• Advanced Urban Planning & Policy Making: City planners can leverage this data to analyze service distribution, identify underserved areas, optimize resource allocation, and strategically plan future infrastructure development. For example, ensuring equitable access to schools or healthcare facilities.
• Geospatial Research & Academic Studies: Researchers can explore urban governance models, assess the efficiency of public service delivery, study the relationship between infrastructure and socio-economic indicators, or analyze accessibility for diverse population groups (e.g., "how many residents live within 1 km of a library?").
• Citizen Empowerment & Public Service Optimization: Developers can build innovative applications that help residents easily locate specific services, receive notifications about nearby facilities, or understand their local administrative points. This data can also inform initiatives aimed at improving citizen access and service experience.
• Data Visualization & Storytelling: Create compelling, interactive maps and dashboards that visually represent Copenhagen's public services, making complex data accessible and engaging for stakeholders, policymakers, and the general public.
• Economic & Social Impact Analysis: Understand the spatial impact of municipal investments, analyze the distribution of job centers in relation to unemployment hotspots, or evaluate the reach of social support programs.

Whether you are a geospatial analyst, an urban planner, a data journalist, a policy maker, a student, or a tech enthusiast, this rich dataset offers an invaluable foundation for understanding, analyzing, and improving the vibrant fabric of Copenhagen's municipal services.

This data set is a result of compiling differing source materials of various vintages.Source material examples used to create and maintain dataset include: BLM 100k Subsurface Maps, Oil and Gas Plats, Coal Plats, Public Land Survey GIS Data (cadnsdi v.2.0), Field Office GIS Data, Compiled 24k USGS Maps, and Land Records.