Facilities and features in Chicago parks. For more information, visit http://www.chicagoparkdistrict.com/facilities/search/. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

Chicago Police district station locations. To view or use these files, compression software and special GIS software, such as ESRI ArcGIS, is required. To download, right-click the "Download" link above and choose "Save link as."

An extensive GIS Viewer, more than a single dataset view. This viewer is designed to be more but not at the ESRI ArcMap level.Address Finder, Mailing Label Tool, Pre-built map themes, datasets organized by themes, Community Information.Works very well on a mobile device.

The Digital Geomorphologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York 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 (sahi_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sahi_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 (sahi_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. 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 (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_geology.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 (sahi_geomorphology_metadata_faq.pdf). Please read the sahi_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. 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: Rutgers University, Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sahi_geomorphology_metadata.txt or sahi_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 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.1 meters or 16.7 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 Google Earth, 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 is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

Schools grounds in Chicago. To view or use these files, compression software, like WinZip, and special GIS software, such as ESRI ArcGIS, is required. The .dbf file may also be opened in Excel, Access or other database programs.

Police beats in Chicago that were effective through December 18, 2012. The current police beats boundaries are always available at https://data.cityofchicago.org/d/aerh-rz74. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Special Service Areas (SSA) boundaries in Chicago. The Special Service Area program is a mechanism used to fund expanded services and programs through a localized property tax levy within contiguous industrial, commercial and residential areas. The enhanced services and programs are in addition to services and programs currently provided through the city. SSA-funded projects could include, but are not limited to, security services, area marketing and advertising assistance, promotional activities such as parades and festivals, or any variety of small scale capital improvements that could be supported through a modest property tax levy. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Point data representing locations of CTA bus stops. See attachment below for information on STATUS and POS fields To view or use these files, compression software and special GIS software, such as ESRI ArcGIS is required. Projected Coordinate System: NAD_1983_StatePlane_Illinois_East_FIPS_1201_Feet

Current police beat boundaries in Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

Access National Hydrography ProductsThe National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee.The NHD is a national framework for assigning reach addresses to water-related entities, such as industrial discharges, drinking water supplies, fish habitat areas, wild and scenic rivers. Reach addresses establish the locations of these entities relative to one another within the NHD surface water drainage network, much like addresses on streets. Once linked to the NHD by their reach addresses, the upstream/downstream relationships of these water-related entities--and any associated information about them--can be analyzed using software tools ranging from spreadsheets to geographic information systems (GIS). GIS can also be used to combine NHD-based network analysis with other data layers, such as soils, land use and population, to help understand and display their respective effects upon one another. Furthermore, because the NHD provides a nationally consistent framework for addressing and analysis, water-related information linked to reach addresses by one organization (national, state, local) can be shared with other organizations and easily integrated into many different types of applications to the benefit of all.Statements of attribute accuracy are based on accuracy statements made for U.S. Geological Survey Digital Line Graph (DLG) data, which is estimated to be 98.5 percent. One or more of the following methods were used to test attribute accuracy: manual comparison of the source with hardcopy plots; symbolized display of the DLG on an interactive computer graphic system; selected attributes that could not be visually verified on plots or on screen were interactively queried and verified on screen. In addition, software validated feature types and characteristics against a master set of types and characteristics, checked that combinations of types and characteristics were valid, and that types and characteristics were valid for the delineation of the feature. Feature types, characteristics, and other attributes conform to the Standards for National Hydrography Dataset (USGS, 1999) as of the date they were loaded into the database. All names were validated against a current extract from the Geographic Names Information System (GNIS). The entry and identifier for the names match those in the GNIS. The association of each name to reaches has been interactively checked, however, operator error could in some cases apply a name to a wrong reach.Points, nodes, lines, and areas conform to topological rules. Lines intersect only at nodes, and all nodes anchor the ends of lines. Lines do not overshoot or undershoot other lines where they are supposed to meet. There are no duplicate lines. Lines bound areas and lines identify the areas to the left and right of the lines. Gaps and overlaps among areas do not exist. All areas close.The completeness of the data reflects the content of the sources, which most often are the published USGS topographic quadrangle and/or the USDA Forest Service Primary Base Series (PBS) map. The USGS topographic quadrangle is usually supplemented by Digital Orthophoto Quadrangles (DOQs). Features found on the ground may have been eliminated or generalized on the source map because of scale and legibility constraints. In general, streams longer than one mile (approximately 1.6 kilometers) were collected. Most streams that flow from a lake were collected regardless of their length. Only definite channels were collected so not all swamp/marsh features have stream/rivers delineated through them. Lake/ponds having an area greater than 6 acres were collected. Note, however, that these general rules were applied unevenly among maps during compilation. Reach codes are defined on all features of type stream/river, canal/ditch, artificial path, coastline, and connector. Waterbody reach codes are defined on all lake/pond and most reservoir features. Names were applied from the GNIS database. Detailed capture conditions are provided for every feature type in the Standards for National Hydrography Dataset available online through https://prd-wret.s3-us-west-2.amazonaws.com/assets/palladium/production/atoms/files/NHD%201999%20Draft%20Standards%20-%20Capture%20conditions.PDF.Statements of horizontal positional accuracy are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For horizontal accuracy, this standard is met if at least 90 percent of points tested are within 0.02 inch (at map scale) of the true position. Additional offsets to positions may have been introduced where feature density is high to improve the legibility of map symbols. In addition, the digitizing of maps is estimated to contain a horizontal positional error of less than or equal to 0.003 inch standard error (at map scale) in the two component directions relative to the source maps. Visual comparison between the map graphic (including digital scans of the graphic) and plots or digital displays of points, lines, and areas, is used as control to assess the positional accuracy of digital data. Digital map elements along the adjoining edges of data sets are aligned if they are within a 0.02 inch tolerance (at map scale). Features with like dimensionality (for example, features that all are delineated with lines), with or without like characteristics, that are within the tolerance are aligned by moving the features equally to a common point. Features outside the tolerance are not moved; instead, a feature of type connector is added to join the features.Statements of vertical positional accuracy for elevation of water surfaces are based on accuracy statements made for U.S. Geological Survey topographic quadrangle maps. These maps were compiled to meet National Map Accuracy Standards. For vertical accuracy, this standard is met if at least 90 percent of well-defined points tested are within one-half contour interval of the correct value. Elevations of water surface printed on the published map meet this standard; the contour intervals of the maps vary. These elevations were transcribed into the digital data; the accuracy of this transcription was checked by visual comparison between the data and the map.

Chicago's central business district boundary. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

Point data representing CTA park and ride locations. Details include number of spaces, cost, and rail station. To view or use these files, compression software and special GIS software, such as ESRI ArcGIS is required.
Projected Coordinate System: NAD_1983_StatePlane_Illinois_East_FIPS_1201_Feet

City boundary of Chicago. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ).

This map and corresponding dataset provide the location, satellite images and square footage of existing green roofs within the City of Chicago. This dataset is in ESRI shapefile format. To view or use these files, compression software and special GIS software, such as ESRI ArcGIS, is required. This information is derived from an analysis of high-spatial resolution (50cm), pan-sharpened, ortho-rectified, 8-band multi-spectral satellite images collected by Digital Globe’s Worldview-2 satellite. The City supplied the consultant with a 2009 City boundary shapefile to determine the required extent of the imagery. Acquisition of three different strips of imagery corresponding to the satellite’s paths was required. These strips of imagery spanned three consecutive months and were collected in August 2010 (90% coverage), September 2010 (5% coverage) and October 2010 (5% coverage). The results of the analysis include overall count of vegetated roofs, their total square footage, and the ratio of required to elective vegetated roofs. A total of 359 vegetated roofs were identified within the City of Chicago. The total square footage of these vegetated roofs was calculated to be approximately 5,469,463 square feet. The ratio of required vegetated roofs to elective vegetative roofs was 297:62 (~5:1). The median size of the vegetated roofs was calculated to be 5,234 square feet.

GIS Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to witness substantial growth over the forecast period, with a notable CAGR of 11.2% from 2024 to 2032. In 2023, the market size was valued at approximately USD 9.1 billion and is expected to reach around USD 23.5 billion by 2032. This growth trajectory is primarily driven by the increasing integration of GIS across various industries, advancements in spatial data analysis technologies, and heightened demand for location-based services. The rising need for urban planning and smart city projects also significantly contributes to the market's expansion, alongside growing investments in infrastructure development across the globe.

Several key factors underpin the robust growth of the GIS software market. Firstly, the surge in demand for spatial data analytics is transforming decision-making processes across sectors such as agriculture, construction, and transportation. GIS software enables organizations to visualize, analyze, and interpret data to understand spatial relationships, patterns, and trends. This capability is crucial for efficient resource management, strategic planning, and effective deployment of assets. Furthermore, the integration of GIS with artificial intelligence and machine learning technologies enhances predictive analytics, enabling more precise forecasting and decision-making, which drives further adoption in both private and public sectors.

Secondly, the expansion of smart city initiatives worldwide is propelling the demand for GIS software. As urban areas continue to grow, there is an increasing need for sophisticated tools that can aid in planning and managing complex infrastructural developments. GIS software plays a pivotal role in urban planning by providing detailed visualization and analysis of spatial data, thereby aiding in effective decision-making concerning transportation, utilities, land use, and environmental management. This is further bolstered by government initiatives aimed at improving urban infrastructure and sustainability, thus contributing significantly to market growth.

Additionally, the growing adoption of location-based services across various industries is another major driver for the GIS software market. These services leverage GIS technology to provide real-time data and analytics, which are essential for navigation, asset tracking, and location-based marketing. The transportation and logistics sectors, in particular, are extensively utilizing GIS for route optimization, fleet management, and logistics planning. Moreover, the proliferation of smartphones and mobile applications has accelerated the demand for these services, further spurring the growth of the GIS software market.

The regional outlook for the GIS software market highlights a varied growth trajectory across different geographies. North America currently holds a significant market share due to the presence of major GIS software vendors and early adoption of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid urbanization, infrastructure development, and increasing investments in smart city projects in countries like China and India are key factors driving the market in this region. Europe also shows promising growth prospects, particularly with the European Union's emphasis on sustainable development and environmental management, which necessitates the use of GIS technology.

Component Analysis

The GIS software market segmentation by component includes both software and services. The software segment is anticipated to hold the largest market share, driven by the increasing adoption of advanced software solutions that offer comprehensive tools for data analysis, mapping, and visualization. Software platforms that integrate GIS with cloud computing, IoT, and AI are seeing heightened demand as they provide more robust, scalable, and efficient solutions for complex spatial data analysis. Companies are continuously innovating to enhance the functionalities of GIS software, which is further propelling the growth of this segment.

Within the software segment, desktop GIS applications continue to dominate due to their widespread use in detailed data analysis and map creation. However, WebGIS and mobile GIS applications are rapidly gaining traction owing to their accessibility and convenience, allowing users to analyze spatial data from anywhere and at any time. This shift is largely attributed to the growing need for real-time data access and the integration

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Building footprints in Chicago. Metadata may be viewed and downloaded at http://bit.ly/HZVDIY. The data can be viewed on the Chicago Data Portal with a web browser. However, to view or use the files outside of a web browser, you will need to use compression software and special GIS software, such as ESRI ArcGIS (shapefile) or Google Earth (KML or KMZ), is required.

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