This metadata record describes the acquisition and production of 1 foot contours for 5 coastal counties Hancock, Harrison, Jackson, Pearl River and Stone. The breaklines were collected from digital imagery with a 15 cmground sample distance (GSD) for the project area for the 1 foot contour area and 30 cm for the 5 foot contour area. All imagery was acquired in spring 2007 and processed during the spring & summer of 2007. The imagery is from a project tasked by Mississippi Geographic Information, LLC (MGI) with Work Orders ED-9 & ED-9A. EarthData International, Inc. was authorized to undertake this project in accordance with the terms and conditions of the professional service agreement between MGI and EarthData International, Inc., dated February 14, 2007.

This dataset contains locations and attributes of University and College, created as part of the DC Geographic Information System (DC GIS) for the Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Information provided by OCTO, EMA, and other sources identified as University Areas and DC GIS staff geo-processed the data. This layer does not represent university areas contained in the campus plans from the DC Office of Zoning.

The MDOT_CO_LRM feature is a complete dataset of roads in the State of Mississippi. All road mileages are referenced to the county they are contained. • This feature contains the State Maintained Routes as well as Local Roads. • All connectivity and topology checks have been performed. • The network contains ramps, frontage roads, intersection turnouts, as well as cross-overs.*** Updated September 2019 ***

The Digital Geomorphic-GIS Map of Cat Island (5-meter accuracy 2007 mapping), Mississippi is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (cati_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (cati_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (cati_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (cati_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cati_geomorphology_metadata.txt or cati_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:11,500 and United States National Map Accuracy Standards features are within (horizontally) 9.7 meters or 31.9 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

This set contains last return LiDAR DATA ( all shot ) orthometric & ellipsoidal elevations, last return LiDAR DATA ( bare earth ) orthometric elevations, first return LiDAR DATA ( digital surface model ) orthometric elevations as well as ARC ID, breakline and 10' contour shape files. Data set also includes color 2' pixel digital ortho tiles and a overall MrSid image of the Mississippi River project.

Updated Continually

City of Newton, MA GIS Data Dictionary

U.S. National Transportation Atlas Railroads represents a comprehensive database of the nation's railway system at 1:100,000 scale. The data set covers the 48 contiguous States plus the District of Columbia within United States.

Mississippi 1:24,000 scale contours from the USGS quad maps. The county contour files were checked for coding of contour type: 1) Index 2) Intermediate, and 3) Supplmemntal. Index contours will draw at 1:1,000,000 scale and intermediate and supplementals will draw at 1:100,000 scale. Index elevations will draw at 1:32,000 and other contours' lelevations will draw at 1:16,000.

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology.

DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the Open Data application. To make changes to this site, please visit https://opendata.arcgis.com/admin/

As GIS and computing technologies advanced rapidly, many indoor space studies began to adopt GIS technology, data models, and analysis methods. However, even with a considerable amount of research on indoor GIS and various indoor systems developed for different applications, there has not been much attention devoted to adopting indoor GIS for the evaluation space usage. Applying indoor GIS for space usage assessment can not only provide a map-based interface for data collection, but also brings spatial analysis and reporting capabilities for this purpose. This study aims to explore best practice of using an indoor GIS platform to assess space usage and design a complete indoor GIS solution to facilitate and streamline the data collection, a management and reporting workflow. The design has a user-friendly interface for data collectors and an automated mechanism to aggregate and visualize the space usage statistics. A case study was carried out at the Purdue University Libraries to assess study space usage. The system is efficient and effective in collecting student counts and activities and generating reports to interested parties in a timely manner. The analysis results of the collected data provide insights into the user preferences in terms of space usage. This study demonstrates the advantages of applying an indoor GIS solution to evaluate space usage as well as providing a framework to design and implement such a system. The system can be easily extended and applied to other buildings for space usage assessment purposes with minimal development efforts.

A map of the University of Mississippi Field Station and accompanying GIS data

U.S. Populated Place Areas represents populated place areas that include census designated places, consolidated cities, and incorporated places within United States identified by the U.S. Census Bureau.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - KNIME Lab Manual (1st ed.). CRC Press. https://doi.org/10.1201/9781003304357 Update Log the dataset and tool for ABM Crime Simulation were updated on August 3, 2023, the toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv R_ArcGIS_Tools.tbx (PCAandFA, SpatialRegressionModel) 9 Louisiana Louisiana.gdb MLR Tools Pro.tbx R_ArcGIS_Tools.tbx (RegionalizationR) 10 SimuCity SimuCity.gdb Garin-Lowry.tbx 11A Columbus Columbus.gdb R_ArcGIS_Tools.tbx (WasteCommuteR) 11B Xiantao XT.gdb R_ArcGIS_Tools.tbx (MiniMaxR, MAEP) 12A Chicago ZoneEffect.gdb 12B BRMSA BRMSAmc.gdb MCSimulation.tbx 13 ABMSIM Data ...

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 U.S. Fish and Wildlife Service Corporate Master Table (CMT) is the official source of Service organization codes and related information. Information in the CMT includes, but is not limited to, organization codes, organization names, Federal Budget Management System (FBMS), cost center codes, fire unit identifiers, program names, mailing and physical/shipping addresses, telephone and fax numbers as well as latitude and longitude coordinates. The CMT enables all Service automated systems to utilize a corporate data set of known quality, eliminating the workload required to maintain each system's data set, and thereby facilitating data sharing. Other customers for the CMT are Service personnel who maintain directories, communicate with Congress and with the Public, maintain World Wide Web sites, etc. These spatial data were created using the information in the CMT. The CMT contains location information on all the offices within the Service that have an organization code. Unstaffed offices and some other facilities may not be included. The latitude and longitude points used are usually the location of the main administrative site. The latitude and longitude data is not completely verified but is the best we have at this time. This data set is intended to give an overview of where USFWS has stations across the United States and Territories, including locations outside the 50 states. It is not intended to be the exact location of every USFWS office. The CMT is primarily used for accounting purposes and therefore one location in the CMT can represent many different offices. Some points are duplicates where a station, most usually an Ecological Field Office, may be associated with more than one USFWS program. This data is updated from an internal authoritative source every night at 2:30am EST.For a direct link to the official Enterprise Geospatial dataset and metadata: https://ecos.fws.gov/ServCat/Reference/Profile/60076.Dataset contact: fwsgis@fws.gov

A developmental version of a overarching webmap used by the NWS to use as the webmap behind a front-facing MapSeries Storymap on https://www.weather.gov/gis/

Polygon layer of Texas college/university campuses. Update Frequency: As NeededSource: Other SourcesSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

These data provide an accurate high-resolution shoreline compiled from imagery of Mississippi Sound, White Harbor to Biloxi Bay, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal...