The research project from which this dataset was produced was designed to help bridge the divide in understanding of the possible environmental causes of breast cancer in the United Kingdom. This divide exists between the official cancer research and treatment world, and other unofficial groups of diverse expertise. The geographic information system methodology used (Geographic Information Systems for Participation, or GIS-P) was intended to increase the understanding of the various positions in the debate both for the researchers, but also more importantly, between the communities of interest. The intention was to stimulate debate through the shared understanding that could be achieved by debating the knowledge and viewpoints expressed through the maps. In this respect, debate stimulation was more important than to capture detailed participatory derived spatial data (as has been the case with previous GIS-P projects). In practice, the process proved problematic, which explains the relatively limited quantity of GIS-P data collected.

This is the final export from the Forest Inventory Module (FIM) system, retired on 6/29/2022.

This layer is a digital inventory of individual forest stands. The data is collected by MNDNR Foresters in each MNDNR Forestry Administrative Area, and is updated on a continuous basis, as needed. Most stands are field checked and their characteristics described. Follows internal MNDNR classification schema. This data originates from the MNDNR's "Forest Inventory Management" system (also referred to as FIM).

This resource was replaced by MNDNR Forest Inventory: https://gisdata.mn.gov/dataset/biota-dnr-forest-inventory

The data provides a summary of the state of development practice for Geographic Information Systems (GIS) software (as of August 2017). The summary is based on grading a set of 30 GIS products using a template of 56 questions based on 13 software qualities. The products range in scope and purpose from a complete desktop GIS systems, to stand-alone tools, to programming libraries/packages.

The template used to grade the software is found in the TabularSummaries.zip file. Each quality is measured with a series of questions. For unambiguity the responses are quantified wherever possible (e.g.~yes/no answers). The goal is for measures that are visible, measurable and feasible in a short time with limited domain knowledge. Unlike a comprehensive software review, this template does not grade on functionality and features. Therefore, it is possible that a relatively featureless product can outscore a feature-rich product.

A virtual machine is used to provide an optimal testing environments for each software product. During the process of grading the 30 software products, it is much easier to create a new virtual machine to test the software on, rather than using the host operating system and file system.

The raw data obtained by measuring each software product is in SoftwareGrading-GIS.xlsx. Each line in this file corresponds to between 2 and 4 hours of measurement time by a software engineer. The results are summarized for each quality in the TabularSummaries.zip file, as a tex file and compiled pdf file.

A stand is a polygon representing a relatively homogenous area of similar cover type. Stands are classified as ‘forested’ (having a canopy of tree species greater than 3 feet tall covering at least 25% of the stand area) or ‘nonforested’ (all stands not meeting the definition of forested). In forested stands, the age class of trees, species composition, basal area stocking, and age structure will be consistent. Nonforested stands are areas of similar species composition.

Operational level forest inventory data was acquired in 2019 and provided the basis for mapping, quantifying and assessing area-wide forest and commercial timber resources and for establishing the AAC for SSE. Forest inventory data from 2019 and the analysis in 2020 provides the following forest management benefits: Updated Timber Type data layer (map) contained in the State’s GIS for SSE Data acquired and analyzed through the forest inventory project was entered into the State’s GIS to create an updated timber type layer (map) of the commercial forest timber base in SSE containing individual timber stands. Updated timber type descriptors for each individual stand include stand species composition, stand density and per acre timber volume. SSE Forest Inventory Report July 17, 2020 4 Using the GIS to analyze the relationships between the commercial timber resource and other forest resources (transportation network, fish and wildlife habitat, cultural resources, etc.) allows the DOF to undertake and complete complex forest planning documents such as the Five-Year Schedules of Timber Sales (FYSTS), and Forest Land Use Plans (FLUPs) used to guide both broad scale and site-specific forest management activities. The GIS also allows DOF to track changes to the commercial timber base resulting from management activities including timber harvest, stand regeneration/reforestation, and timber stand improvement projects such as precommercial tree thinning. Updated Annual Allowable Cut for SSE The GIS timber type map for SSE, updated with the 2019 forest inventory data, formed the basis for area (acreage) and timber volume (board feet) figures necessary to calculate an updated AAC. The new GIS timber type map and associated data files along with newly available LiDAR data provided the raw data necessary to perform the growth and yield modelling to estimate timber volume and characteristics in the developing young growth stands over the course of the rotation.

This point feature layer displays locations of site development projects for the City of Tallahassee's (COT) Site Plan Review Agenda. This layer is regenerated every weekday by TLCGIS using a tabular extract from COT's permitting system (Cityworks).This layer supports multiple production applications.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. James W. Sewall Company developed a complete GIS coverage for the park and revised the preliminary vegetation map classes to better match the results from the cluster analysis and NMS ordination. Polygons representing vegetation stands were digitized on-screen in ArcGIS 8.3, and later in ArcMap 9.1 and 9.2, using lines drawn on the acetate overlays, base layers of 1:8,000 CIR aerial photography, orthorectified photo composite image, and plot location and data. The minimum map unit used was 0.5 ha (1.24 ac). Stereo pairs were used to double check stand signatures during the digitizing process. Photo interpretation and polygon digitization extended outside the NPS boundary, especially where vegetation units were arbitrarily truncated by the boundary. Each polygon was attributed with the name of a vegetation map class or an Anderson Level II land use category based on plot data, field observations, aerial photography signatures, and topographic maps. Data fields identifying the USNVC association inclusions within the vegetation map class were attributed to the vegetation polygons in the shapefile. The GIS coverages and shapefiles were projected to Universal Transverse Mercator (UTM) Zone 19 North American Datum 1983 (NAD83). FGDC compliant metadata (FGDC 1998a) were created with the NPS-MP ESRI extension and included with the vegetation map shapefile. A photointerpretation key to the map classes for the 2006 draft vegetation map is included as Appendix A. The composite vegetation coverage was clipped to the NPS 2002 MIMA boundary shapefile for accuracy assessment (AA). After the 2006 vegetation map was completed, the thematic accuracy of this map was assessed.

This dataset consists of summer temperature metrics for Boston, MA. These heat metrics summarize six CAPA Urban Heat Watch program temperature and heat index datasets using geographical boundaries from the Census Tract (CT) layer. Heat datasets were created by Museum of Science, Boston, and the Helmuth Lab at Northeastern University. Heat metrics are presented in the attribute table as mean values of each Heat Watch program dataset for all hexagon features. The six heat values included in this table are July 2019 temperature and heat index in degrees Fahrenheit for each of 3 1-hour periods -- 6 a.m., 3 p.m., and 7 p.m. EDT. The geographic boundaries used to summarize the heat metrics are current as of 2019.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The Division of Forestry has been managing forest resources for many years in the Tanana Valley area. The purpose of this GIS layer, is to create a spatial coverage of vegetation on state lands to aid in forest management.

This National Geographic Style Map (World Edition) web map provides a reference map for the world that includes administrative boundaries, cities, protected areas, highways, roads, railways, water features, buildings, and landmarks, overlaid on shaded relief and a colorized physical ecosystems base for added context to conservation and biodiversity topics. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri, National Geographic or any governing authority.This basemap, included in the ArcGIS Living Atlas of the World, uses the National Geographic Style vector tile layer and the National Geographic Style Base and World Hillshade raster tile layers.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

Natural Resource Inventory/Forest Stand Delineations (NRI/FSDs) and Forest Conservation Plan Exemptions (FCPEs) are intended to document and map the locations of all existing environmental on-site features, including but not limited to topography, wetlands, floodplains, streams, and specimen trees. The difference between an NRI/FSD and an FCP Exemption is the way the plans are used in our planning process. NRI/FSDs must be approved before a Forest Conservation Plan application can be submitted. FCP Exemptions, once confirmed, exempt the applicant from having to submit a Forest Conservation Plan as part of future applications. For FCP Exemptions, applicants must identify which section of Forest Conservation Law 22-5A they believe they are exempt under. For both NRI/FSDs and FCP Exemptions there are three possible levels of detail required: Full NRI/FSDs: Required where forest exists on the property and is impacted by the proposed activity. Full NRI/FSDs must include a forest stand delineation and thorough description of the forest characteristics. Simplified NRI: Appropriate where no forest exists on the property, and therefore does not need to include a forest stand delineation or description of forest characteristics. Existing Conditions Plan: Simplified plan showing only basic site details in addition to extent of disturbance proposed. Applicants must receive permission from Montgomery Planning Staff to submit an Existing Conditions Plan. For more details: https://montgomeryplanning.org/development/development-applications/nri-fsd-fcp-exemption/ For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

This dataset displays aspen stands in the Warner Mountains south of State Highway 299. The 1946 and 1994 data are presented as polygons depicting aspen stands derived from aerial photos taken in 1946 and 1994, respectively, while the 2002 data includes the aspen stand delineations and their corresponding vegetation data (i.e., size distribution of aspen, the canopy and shrub cover, the number of species of grasses and forbs, and the ratio of conifers to aspens). The polygons ranged in size from less than 0.2 acres to more than 1,130 acres. There are 2103 records, one for each aspen stand mapped and sampled. All data were collected and summarized by Aaron Di Orio, California Department of Fish and Wildlife. This is one of three data sets for a study investigating change in the size of aspen stands since 1946. Other available data sets are 1946 and 1994 aerial photo mosaics.

Vegetation delineations based on photo interpretation and formal vegetation classification plus change detection. This update by the Geographical Information Center (GIC) of the North State Planning and Development Collective at California State University, Chico is part of an ongoing triennial vegetation monitoring program for the Suisun Marsh. The project tracks changes in the Suisun Marsh vegetation over time to fulfill specific permit requirements of the Suisun Marsh Plan of Protection of 1984, the Suisun Marsh Preservation Agreement of 1986, and the 2015 Suisun Marsh Preservation Agreement. This is the seventh update using the current mapping standards originally implemented in 1999. All of the previous vegetation maps from 1999 to 2015 can be viewed and downloaded using the online California Department of Fish and Wildlife (CDFW) Biogeographic Information and Observation System (BIOS); the links to the associated reports are included in the map metadata. Minimum mapping unit (MMU): Typically, the minimum mapping size is 0.25 acres. However, the photo interpreters use their best judgment to determine if a stand below 0.25 acre should be separately delineated. For example, a smaller polygon would be appropriate for any new visible occurrence of a non-native species of concern, such as Phragmites australis, Arundo donax, Carpobrotus edulis, Eucalyptus spp., and Lepidium latifolium. Minimum mapping width: There are many long and narrow polygons within the Suisun Marsh study area, most of which are roads, ditches, levees, and sloughs. The minimum mapping width is typically 10 feet; however, if small sections of a stand fell below the minimum width, the polygon was not split. More information can be found in the project report, which is bundled with the vegetation map published for BIOS here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2963.zip, and the report is available for download separately at: https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=218048.

The global GIS Receiver market is experiencing robust growth, driven by increasing adoption in diverse sectors like surveying, construction, and precision agriculture. The market, valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising demand for precise geospatial data is creating significant opportunities for GIS receiver manufacturers. Secondly, technological advancements, such as the integration of improved GNSS technologies and higher accuracy sensors, are enhancing the capabilities and appeal of GIS receivers. Furthermore, the increasing penetration of affordable and user-friendly GIS software solutions is broadening the market's addressable audience. The market segmentation reveals a healthy demand across various receiver types (all-in-one and stand-alone) and applications (survey and mapping being dominant). Competition is intense, with established players like Hexagon, Trimble, and Topcon facing challenges from emerging regional competitors. The market's future growth trajectory is significantly influenced by factors like government investments in infrastructure projects, the expansion of smart cities initiatives, and the growing adoption of precision agriculture techniques. While the market presents significant opportunities, certain restraints need to be considered. The high initial investment cost associated with procuring advanced GIS receivers can act as a barrier for entry, particularly for small and medium-sized enterprises (SMEs). Furthermore, the market's growth is susceptible to fluctuations in economic conditions and government spending patterns. Another challenge arises from the complexities involved in data processing and interpretation, requiring specialized skills and expertise. However, the ongoing development of more user-friendly software and training programs are expected to alleviate this concern. The geographical distribution of the market shows a relatively even spread, with North America and Europe maintaining a strong presence, followed by a rapidly expanding Asia-Pacific region. This report provides a detailed analysis of the global GIS receiver market, a sector projected to exceed $5 billion in revenue by 2028. We delve into market concentration, key trends, dominant regions, product insights, and future growth catalysts. This in-depth study is invaluable for businesses involved in surveying, mapping, construction, and other sectors leveraging GNSS technology.

These data are a product of a multi-year effort by the FHTET (Forest Health Technology Enterprise Team) Remote Sensing Program to develop raster datasets of forest parameters for each of the tree species measured in the Forest Service’s Forest Inventory and Analysis (FIA) program. This dataset was created to support the 2013–2027 National Insect and Disease Risk Map (NIDRM) assessment. The statistical modeling approach used data-mining software and an archive of geospatial information to find the complex relationships between GIS layers and the presence/abundance of tree species as measured in over 300,000 FIA plot locations. Unique statistical models were developed from predictor layers consisting of climate, terrain, soils, and satellite imagery. Modeled basal area (BA) and stand density index (SDI) datasets for individual tree species were further post-processed to 1) match BA and SDI histograms of FIA data, 2) ensure that the sum of individual species BA and SDI on a pixel did not exceed separately modeled total for all species BA and SDI raster datasets, 3) derive additional tree parameters like quadratic mean diameter and trees per acre. With Landsat image collection dates ranging from 1985 to 2005, and a mean collection date for treed areas of 2002, and FIA plot data generally ranging from 1999 to 2005, the vintage of the base parameter datasets varies based on location, but can be roughly considered as 2002

CDFW BIOS GIS Dataset, Contact: Chris Stermer, Description: The database represents 702 point locations and associated stand assessment data collected in aspen stands in the in the Eagle Lake Ranger District, Lassen National Forest. Data were gathered during the summers of 2001-2005. Observations were conducted by trained Forest Service crews. Assessments were conducted in aspen stands in the region, or stands identified during field surveys. This dataset is considered to be a complete inventory of aspen in these watersheds.

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

TIGER road data for the MSA. When compared to high-resolution imagery and other transportation datasets positional inaccuracies were observed. As a result caution should be taken when using this dataset. TIGER, TIGER/Line, and Census TIGER are registered trademarks of the U.S. Census Bureau. ZCTA is a trademark of the U.S. Census Bureau. The Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States, Puerto Rico, and the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The boundary information in the TIGER/Line files are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement. The Census 2000 TIGER/Line files do NOT contain the Census 2000 urban areas which have not yet been delineated. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

   This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase.


   The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive.


   The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders.


   Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.