This dataset has been deprecated. Please use our 2017 Countywide Contours instead.These data comprise of one foot contours showing the elevation of Lake County, IL corresponding to the orthometric heights of the bare surface of the county (no buildings or vegetation cover). These data were developed from LIDAR data obtained between April 16, 2007 and May 07, 2007, and further supplemented by break lines collected from aerial photography taken at the same time. The contours and the aerial photography meet National Map Accuracy Standards for a 1 inch = 100 foot product. The contours have been created to be cartographically pleasing, as such some generalization has been introduced as expected. The contours have been tested against the original LIDAR points and conforms to the American Society for Photogrammetry and Remote Sensing (ASPRS) Specifications and Standards Committee, 1990, ASPRS Accuracy Standards for Large-Scale Maps, CLASS 1 map accuracy. The vertical datum is the North American Vertical Datum of 1988. The U.S. Army Corps of Engineers Engineering and Design Manual for Photogrammetric Production recommends that data intended for this usage scale be used for any of the following purposes: route location, preliminary alignment and design, preliminary project planning, hydraulic sections, rough earthwork estimates, or high-gradient terrain / low unit cost earthwork excavation estimates. The manual does not recommend that these data be used for final design, excavation and grading plans, earthwork computations for bid estimates or contract measurement and payment. Contour data is used by county agencies to study drainage issues such as flooding and erosion. Customers interested in the actual LiDAR data may download it at no cost from the USGS Center for LiDAR Information Coordination and Knowledge. These files are tiled by PLSS section and grouped by township and format for easier download. Please note that this dataset is available in for GIS in Esri shapefile format as well as for CAD in AutoCAD DWG and MicroStation DGN. We are unable to provide any support for CAD users beyond providing the data.

Cad flooding data areas. This item contains data for possible flooding locations for web map https://dallasgis.maps.arcgis.com/home/item.html?id=7c921878167d488b8d88b7da45caae74This dataset is not currently being updated.

Select Aggregate Planimetric CAD Vectors from set of 2001-2019 Community Profile AutoCAD files - Initial release version Oct 26, 2023This is an AGOL-hosted Feature Service - hence text point labels only display horizontallySee these related Services (circa Oct 2023):DCRA Community Profile Availability and Map Sheet Outlines with PDF Maps as downloadable attachmentsDCRA Community Profile Aerial Imagery (Community Hi-Res Scale)DCRA Community Profile Aerial Imagery (Community General Area Low-Res Scale)State of Alaska Maxar RGB Satellite ImageryHigh Level Documentation as a PowerPoint Presentation in PDF form

Ravine & Natural Feature Protection area and limit as regulated by City of Toronto Municipal Code Chapter 658 - Ravine & Natural Feature Protection. Please note: Dataset is limited to the City of Toronto geographical boundary. Dataset works best if you open it with GIS/CAD software and reference these layers: City of Toronto Street Centreline, Address Points and Property Boundaries. If your original design drawing has been created without a coordinate system, you will need to reference the city's datasets and then move, scale and/or rotate your data to line up with the property limits for the property you are working with.

NOAA's ENC Direct to GIS web portal provides comprehensive access to display, query, and download all available NOAA ENC data in a variety of GIS/CAD formats for non-navigational purposes using Internet mapping service technology. An area of the bottom of a body of water which has been deepened by dredging.(IHO Dictionary, S-32, 5th Edition, 1462)

Linn County, Iowa contours clipped to the approximate area of PLSS Township 83 Range 5. This dataset is in AutoCAD (.dwg) format.This dataset is updated as needed, and not on a regular basis.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps and apps of interest from various departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

This study used crime count data from the Pittsburgh, Pennsylvania, Bureau of Police offense reports and 911 computer-aided dispatch (CAD) calls to determine the best univariate forecast method for crime and to evaluate the value of leading indicator crime forecast models. The researchers used the rolling-horizon experimental design, a design that maximizes the number of forecasts for a given time series at different times and under different conditions. Under this design, several forecast models are used to make alternative forecasts in parallel. For each forecast model included in an experiment, the researchers estimated models on training data, forecasted one month ahead to new data not previously seen by the model, and calculated and saved the forecast error. Then they added the observed value of the previously forecasted data point to the next month's training data, dropped the oldest historical data point, and forecasted the following month's data point. This process continued over a number of months. A total of 15 statistical datasets and 3 geographic information systems (GIS) shapefiles resulted from this study. The statistical datasets consist of Univariate Forecast Data by Police Precinct (Dataset 1) with 3,240 cases Output Data from the Univariate Forecasting Program: Sectors and Forecast Errors (Dataset 2) with 17,892 cases Multivariate, Leading Indicator Forecast Data by Grid Cell (Dataset 3) with 5,940 cases Output Data from the 911 Drug Calls Forecast Program (Dataset 4) with 5,112 cases Output Data from the Part One Property Crimes Forecast Program (Dataset 5) with 5,112 cases Output Data from the Part One Violent Crimes Forecast Program (Dataset 6) with 5,112 cases Input Data for the Regression Forecast Program for 911 Drug Calls (Dataset 7) with 10,011 cases Input Data for the Regression Forecast Program for Part One Property Crimes (Dataset 8) with 10,011 cases Input Data for the Regression Forecast Program for Part One Violent Crimes (Dataset 9) with 10,011 cases Output Data from Regression Forecast Program for 911 Drug Calls: Estimated Coefficients for Leading Indicator Models (Dataset 10) with 36 cases Output Data from Regression Forecast Program for Part One Property Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 11) with 36 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Estimated Coefficients for Leading Indicator Models (Dataset 12) with 36 cases Output Data from Regression Forecast Program for 911 Drug Calls: Forecast Errors (Dataset 13) with 4,936 cases Output Data from Regression Forecast Program for Part One Property Crimes: Forecast Errors (Dataset 14) with 4,936 cases Output Data from Regression Forecast Program for Part One Violent Crimes: Forecast Errors (Dataset 15) with 4,936 cases. The GIS Shapefiles (Dataset 16) are provided with the study in a single zip file: Included are polygon data for the 4,000 foot, square, uniform grid system used for much of the Pittsburgh crime data (grid400); polygon data for the 6 police precincts, alternatively called districts or zones, of Pittsburgh(policedist); and polygon data for the 3 major rivers in Pittsburgh the Allegheny, Monongahela, and Ohio (rivers).

The Right of Way Map Footprint is a GIS data set created to represent the outer footprint or extent of a right of way map (including footprints for both Vector (CAD) and Raster Images). The purpose is to aid the user in more rapidly identifying the desired map for a specific area of interest relative to other maps, roads, landmarks, etc. This data set is developed and maintained on a statewide basis. It does not include geo-referenced representations of right of way maps themselves.

NOAA's Electronic Navigational Charts (NOAA ENCs) have been developed to support the marine transportation infrastructure and coastal management. The NOAA ENCs are in S-57, a data standard developed by the International Hydrographic Organization (IHO) to be used for the exchange of digital hydrographic data. NOAA ENCs can be used in Geographic Information Systems, (GIS) allowing for broader public access. Many GIS's, however cannot read an ENC's native S-57 format to address this problem. NOAA's ENC Direct to GIS web portal provides comprehensive access to display, query, and download all available large scale NOAA ENC data in a variety of GIS/CAD formats for non-navigational purposes using Internet mapping service technology. Nautical chart features contained within an NOAA ENC provide a detailed representation of the U.S. coastal and marine environment. This data includes coastal topography, bathymetry, landmarks, geographic place names and marine boundaries. Features in an NOAA ENC are limited in that they only represent the geographic region that is depicted in that particular NOAA ENC. By aggregating nautical features from all NOAA ENCs in the creation of GIS data, a contiguous depiction of the U.S coastal and marine environment is achieved.

This data set was collected to create a comprehensive platform to visualize SWP owned land. The parcels were created from record parcel information displayed on Department CAD maps known as “Property Management Maps”. This CAD data was then converted to a compatible GIS source and imported as parcels. Each parcel displays the pertinent information regarding the Departments ownership rights and the time that it was acquired or granted. This data set was created to improve the efficiently of accessing and organize the departments land records.

This file contains polygon data representing service areas that are part of public water supply systems within Westchester County, New York, based upon geographic data in GIS or CAD format provided by water supply systems and integrated into a common countywide feature class in 2012. Not all areas in the county served by public water supply systems are covered, and the accuracy of the geographic representations and attributes included in this dataset is dependent upon the accuracy of the source data, which has not been independently verified by Westchester GIS .

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. Dataset contains framework layers compiled for representation on state reference map, scale 1:1.5 million. Line and polygon features only. Road, rail, waterbody and watercourse themes included. State coastline not included. ## Purpose Can be used as a framework layer for whole of state mapping or for a generalised framework for regional mapping. Not suitable for analysis. ## Dataset History Information was compiled and digitised in generalised form from 1:250 000 scale hard copy maps. The individual CAD files were combined into seamless form and converted to Lambert Conformal Conic projection, standard parallels 29 degrees and 35 degrees S, central meridian 135 degrees E. Subsequently the information was converted to GIS format and re-projected to the state standard LCC projection. ## Dataset Citation SA Department of Environment, Water and Natural Resources (2015) Topography - State Refence Map layers - PED. Bioregional Assessment Source Dataset. Viewed 12 October 2016, http://data.bioregionalassessments.gov.au/dataset/431f9e07-7436-4820-9bfd-c90a0a6cc293.

Dataset Description: Washington Grocery Store Locations

This dataset contains detailed information about the locations and operational status of grocery stores in Washington, spanning multiple years. It includes both spatial and temporal data, offering a comprehensive view of how grocery stores are distributed and have evolved over time. Below is a breakdown of the columns included in the dataset:

1. X, Y: Geographic coordinates (latitude and longitude) representing the store's location in the dataset.

2. STORENAME: The name of the grocery store.

3. ADDRESS: The physical address of the grocery store.

4. ZIPCODE: The ZIP code of the store’s location.

5. PHONE: The contact phone number for the store.

6. WARD: The local government ward in which the store is located.

7. SSL: A unique identifier or code related to the store, possibly referring to specific data collection attributes.

8. NOTES: Additional comments or information about the store.

9. PRESENT: Temporal indicators showing the presence (likely open or closed) of each store across various years. These columns provide insights into the longevity and temporal trends of grocery store operations.

10. GIS_ID: A unique identifier for geographic information system (GIS) data.

11. XCOORD, YCOORD: Coordinates (likely more specific) used for spatial data analysis, providing the exact location of the store.

12. MAR_ID: A unique identifier for marketing or regional analysis purposes.

13. GLOBALID: A global unique identifier for the store data.

14. CREATOR: The individual or system that created the data entry.

15. CREATED: Timestamp showing when the data entry was created.

16. EDITOR: The individual or system that edited the data entry.

17. EDITED: Timestamp showing when the data entry was last edited.

18. SE_ANNO_CAD_DATA: Specific annotation or data related to CAD (computer-aided design), possibly linked to store location details.

19. OBJECTID: A unique identifier for the object or record within the dataset.

Insights We Can Extract:

• Geographic Distribution: By analyzing the X and Y coordinates along with ZIP codes and wards, we can identify where grocery stores are concentrated and map areas with high or low store density.
• Temporal Trends: The data in the "PRESENT" columns helps us track the opening and closure patterns of grocery stores over time, providing insights into market trends and store longevity.
• Service Gaps: We can identify areas with no grocery stores, possibly indicating food deserts or underserved communities, by mapping the stores and comparing coverage across ZIP codes and wards.
• Operational Trends: By analyzing the temporal data and comparing store turnover, we can uncover patterns in the longevity or turnover of grocery stores.
• Urban Planning and Accessibility: This dataset could help us assess whether the location of grocery stores aligns with urban infrastructure like transportation routes or population density, which could inform policy decisions to improve grocery access.

This dataset is invaluable for urban planners, policymakers, and business stakeholders looking to improve food access and urban infrastructure.

DescriptionThe baseline features in this dataset were created by a vendor for CDOT during the ROW/real property data modernization project which began in 2015. In March 2020 CDOT took over maintenance and life cycle of the data. The source files used to create the baseline dataset were right of way project plans. The features in this dataset were either digitized from georeferenced PDF source files or created directly from the source Microstation DGN files using the CDOT CAD to GIS conversion tool. Any data gaps along state highways should be assumed to be missing right of way areas where no source files could be located.Last UpdateOngoingUpdate FrequencyAs NeededData OwnerDivision of Transportation DevelopmentData ContactGIS Support UnitCollection MethodProjectionNAD 1983 / UTM Zone 13NCoverage AreaStatewideTemporalDisclaimer/LimitationsThis real property and/or right-of-way geographic information system (GIS) data and any related documents are for reference only and may not be suitable for legal, engineering, or surveying purposes. The Colorado Department of Transportation, and its employees and agents, make no warranty, express or implied, as to the accuracy, completeness, or usefulness of any information and assumes no liability for errors or consequences from use. Verifying the accuracy, completeness or usefulness of this data is the responsibility of the user.

Levee stations, usually in feet but in some cases miles, snapped to 2017 Delta levee centerlines (derived from the 2017 Delta LiDAR). Base source for station locations are surveyed field markers on the levees or distance-derived CAD files, in either case as supplied by local maintaining agency's engineers. DWR collected station location data and snapped the stations into the levee centerline file from 2012. After updated levee centerlines were created, the existing points were snapped to the new lines. So there is some small difference between the supplied station locations, previous station locations and these station locations. In some cases, multiple series of stations exist for a district, generally associated with distinct waterways. Also, district levees may be demarked in feet or in miles. The label fields are simply cartographic support, the label data are identical in all cases, but are provided to support fast labeling at more infrequent intervals as needed. Stationing is not as simple as it may seem. In some cases, multiple sets of stationing exist for a district's levees (see Sherman Island for example). What this dataset intends to represent is the current stationing used by District engineers for that District on levee maintenance and improvement projects. As changes are made to the stationing, and the new stationing data become available to the Levee Program, they will be added to this database. Some islands also have separate groups of stations for various parts of the district. This version is current as of 03/24/2020. Source of the original levee stationing is DWR Delta Levees Program, compiled from data provided by internal files, from CSU Chico State, MBK Engineers, KSN Engineers, Siegfried Engineers, Malani & Associates, Green Mountain Engineers, and DCC Engineers. Processing work done by CA DWR, Division of Engineering, Geodetic Branch, Geospatial Data Support Section, specifically by Arina Ushakova (Research Data Analyst I), and initial QC by Joel Dudas (Senior Engineer, Water Resources).

A daily count of 911 Fire and EMS calls received by Suffolk County Fire Rescue and Emergency Services in 2017. Data includes a daily count of 911 calls by call description.

Geospatial data about Land cover database v4 0 class orders. Export to CAD, GIS, PDF, CSV and access via API.

Linn County, Iowa contours clipped to the approximate area of PLSS Township 83 Range 6. This dataset is in AutoCAD (.dwg) format.This dataset is updated as needed, and not on a regular basis.Additional ResourcesVisit Linn County, Iowa on the web.Visit Linn County, Iowa GIS on the web.Visit the Linn County, Iowa GIS portal. This site is updated as needed to reflect maps and apps of interest from various departments.Contact InformationQuestions? Contact the GIS Division by phone at 319.892.5250 or by email.

The PLSS Quarter Section Reference is a companion feature class for the PLSS Reference Grid that provides a quarter section division of the sections for reference only. This data set represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. This data set includes the following: PLSS Fully Intersected (all of the PLSS feature at the atomic or smallest polygon level), PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non rectangular components of the PLSS) Meandered Water, Corners and Conflicted Areas (known areas of gaps or overlaps between Townships or state boundaries). The Entity-Attribute section of this metadata describes these components in greater detail.

This dataset is a compilation of ownership rights represented as parcels owned by the Sacramento & San Joaquin Drainage District of the State of California (SSJDD), under the authority of the Central Valley Flood Protection Board (CVFPB). The parcels include geospatial data and links to related source documents and were created from DWR's cadastral records currently represented as digital GIS, CAD, and scanned documents as well as hardcopy records stored in binders and filing cabinets at DWR headquarters. DWR requires GIS based parcel data to manage land records defining the location of SSJDD land rights and easements for the CVFPB. The GIS representation of land records provides an index to property records, and CAD COGO based mapping will enable the CVFPB and other entities to more efficiently identify property rights. This data set was created to improve the efficiency accessing the land records and the organization of the land records. It is anticipated this data set will serve as the authoritative source for the next 50-100 years when managed with incremental updates reflecting ongoing updates to parcel ownership and rights. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Rights attributes (annotated with "PART OF RIGHTS ATTRIBUTE GROUP: " in the Column Definition within the Entity and Attribute section of this metadata) are restricted to use by the CVFPB and DWR's Geodetic and Real Estate branches. Please see 'Distribution Info' for details. The rest of the attributes may be consumed publicly.