Columbus Communities is a boundary layer that is be used by city departments for planning and reporting purposes. The boundaries represent areas generally recognized as a "community", which often comprise a number of neighborhoods. The layer is an update to and replaces the Community Planning Areas layer. It utilizes area commission boundaries when an area commission exists, but is not intended to replace or be used in the place of them.

Polygon vector map data covering corporate boundaries for Columbus, Ohio containing 1 feature.

Boundary GIS (Geographic Information System) data is spatial information that delineates the geographic boundaries of specific geographic features. This data typically includes polygons representing the outlines of these features, along with attributes such as names, codes, and other relevant information.

Corporate boundaries display the incorporated areas for a city or region.

Boundary GIS data is used for a variety of purposes across multiple industries, including urban planning, environmental management, public health, transportation, and business analysis.

Available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

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This map layer shows commercial community reinvestment areas in the City of Columbus. Community reinvestment areas are economic development tools that provide real property tax exemptions for property owners who renovate existing or construct new buildings.

Sanborn Fire Insurance maps contain a wealth of building-level information about U.S. cities dating back to the late 19th century. They are a valuable resource for studying changes in urban environments, such as the legacy of urban highway construction and urban renewal in the 20th century. However, it is a challenge to automatically extract the building-level information effectively and efficiently from Sanborn maps because of the large number of map entities and the lack of appropriate computational methods to detect these entities. This paper contributes to a scalable workflow that utilizes machine learning to identify building footprints and associated properties on Sanborn maps. This information can be effectively applied to create 3D visualization of historic urban neighborhoods and inform urban changes. We demonstrate our methods using Sanborn maps for two neighborhoods in Columbus, Ohio, USA that were bisected by highway construction in the 1960s. Quantitative and visual analysis of the results suggest high accuracy of the extracted building-level information, with an F-1 score of 0.9 for building footprints and construction materials, and over 0.7 for building utilizations and numbers of stories. We also illustrate how to visualize pre-highway neighborhoods.

FAF domestic region level datasets and products provide information for states, state portions of large metropolitan areas, and remainders of states. Metropolitan areas consist of Metropolitan Statistical Areas or Consolidated Statistical Areas as defined by the Office of Management and Budget. When a metropolitan area is entirely within a state or when a state's portion of a multi-state metropolitan area is large enough to support the sampling procedures in the Commodity Flow Survey, the area becomes a separate FAF region. Small single-state metropolitan areas and small portions of a multi-state metropolitan area are part of the State or Remainder of State. FAF has two metropolitan areas that are each divided into three FAF regions, four that are each divided into two FAF regions, and several that have small pieces combined with States or Remainders of States.

© United States Department of Transportation, Federal Highway Administration. For more information, see the site http://www.ops.fhwa.dot.gov/freight/freight_analysis/faf/faf3/userguide/index.htm This layer is sourced from maps.bts.dot.gov.

The spatial component of the FAF network is derived from National Highway System Version 2009.11 and contains state primary and secondary roads, National Highway System (NHS), National Network (NN) and several intermodal connectors as appropriate for the freight network modeling. The network consists of over 447,808 miles of equivalent road mileage. The data set covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii. The nominal scale of the data set is 1:100,000 with a maximal positional error of ±80 meters.

© ederal Highway Administration Office of Freight Management and Operations and the Battelle Memorial Institute, Columbus, OH