A Right-of-Way Construction Permit is required for any work performed within the City of Richardson's Right-of-Way or Easements. Work must be performed in accordance with local Ordinances and Right of Way Standard Construction Details. This zipfile contains a layer file and a file geodatabase with two feature classes. The 'Districts' polygon feature class is just for reference so that the applicant can determine which district the new asset is in (the layer file will have the saved symbology for ease of use). The 'WirelessFacility' point feature class is where the applicant will edit and input the new asset. There are domains on this feature class which will act as 'drop-downs' when editing.Be sure to unzip the file after downloading so that it can be viewed in ArcMap or ArcGIS Pro.For more information about the geodatabase download the ROW GIS Data Template Instructions.

The Digital Geologic Sample Localities of Great Smoky Mountains National Park and Vicinity, Tennessee and North Carolina consists of geologic sample localities mapped as point features. The data were completed as a component of the Geologic Resources Evaluation (GRE) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). The data were captured, grouped and attributed as per the NPS GRE Geology-GIS Geodatabase Data Model v. 1.3.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The data layer is available as a feature class in a 9.1 personal geodatabase (grsm_geology.mdb). The Geologic Sample Localities (GRSMGSL) GIS data layer is also available as a coverage export (.E00) file (GRSMGSL.E00), and as a shapefile (.SHP) file (GRSMGSL.SHP). Each GIS data format has an ArcGIS 9.1 layer (.LYR) file (GRSMGSL_GDB.LYR (geodatabase feature class), GRSMGSL_COV.LYR (coverage), GRSMGSL_SHP.LYR (shapefile) with map symbology that is included with the GIS data. See the Distribution Information section for additional information on data acquisition. The GIS data projection is NAD83, UTM Zone 17N. The data is within the area of interest of Great Smoky Mountains National Park.

These are used in the documentation examples of PolarToolkit to show the profile plotting function capabilities.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Sample template for supply of 3 waters data to Palmerston North City Council.Sample Contents:Water Mains, Service Lines and FixturesSewer Mains, Service Lines and FixturesStorm Mains, Service Lines and Fixtures

Tags

   survey, environmental behaviors, lifestyle, status, PRIZM, Baltimore Ecosystem Study, LTER, BES




   Summary


   BES Research, Applications, and Education


   Description


   Geocoded for Baltimore County. The BES Household Survey 2003 is a telephone survey of metropolitan Baltimore residents consisting of 29 questions. The survey research firm, Hollander, Cohen, and McBride conducted the survey, asking respondents questions about their outdoor recreation activities, watershed knowledge, environmental behavior, neighborhood characteristics and quality of life, lawn maintenance, satisfaction with life, neighborhood, and the environment, and demographic information. The data from each respondent is also associated with a PRIZM� classification, census block group, and latitude-longitude. PRIZM� classifications categorize the American population using Census data, market research surveys, public opinion polls, and point-of-purchase receipts. The PRIZM� classification is spatially explicit allowing the survey data to be viewed and analyzed spatially and allowing specific neighborhood types to be identified and compared based on the survey data. The census block group and latitude-longitude data also allow us additional methods of presenting and analyzing the data spatially. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 



   The BES 2003 telephone survey was conducted by Hollander, Cohen, and McBride from September 1-30, 2003. The sample was obtained from the professional sampling firm Claritas, in order that their "PRIZM" encoding would be appended to each piece of sample (telephone number) supplied. Mailing addresses were also obtained so that a postcard could be sent in advance of interviewers calling. The postcard briefly informed potential respondents about the survey, who was conducting it, and that they might receive a phone call in the next few weeks. A stratified sampling method was used to obtain between 50 - 150 respondents in each of the 15 main PRIZM classifications. This allows direct comparison of PRIZM classifications. Analysis of the data for the general metropolitan Baltimore area must be weighted to match the population proportions normally found in the region. They obtained a total of 9000 telephone numbers in the sample. All 9,000 numbers were dialed but contact was only made on 4,880. 1508 completed an interview, 2524 refused immediately, 147 broke off/incomplete, 84 respondents had moved and were no longer in the correct location, and a qualified respondent was not available on 617 calls. This resulted in a response rate of 36.1% compared with a response rate of 28.2% in 2000. The CATI software (Computer Assisted Terminal Interviewing) randomized the random sample supplied, and was programmed for at least 3 attempted callbacks per number, with emphasis on pulling available callback sample prior to accessing uncalled numbers. Calling was conducted only during evening and weekend hours, when most head of households are home. The use of CATI facilitated stratified sampling on PRIZM classifications, centralized data collection, standardized interviewer training, and reduced the overall cost of primary data collection. Additionally, to reduce respondent burden, the questionnaire was revised to be concise, easy to understand, minimize the use of open-ended responses, and require an average of 15 minutes to complete. 


   The household survey is part of the core data collection of the Baltimore Ecosystem Study to classify and characterize social and ecological dimensions of neighborhoods (patches) over time and across space. This survey is linked to other core data, including US Census data, remotely-sensed data, and field data collection, including the BES DemSoc Field Observation Survey. 


   Additional documentation of this database is attached to this metadata and includes 4 documents, 1) the telephone survey, 2) documentation of the telephone survey, 3) metadata for the telephone survey, and 4) a description of the attribute data in the BES survey 2003 survey.


   This database was created by joining the GDT geographic database of US Census Block Group geographies for the Baltimore Metropolitan Statisticsal Area (MSA), with the Claritas PRIZM database, 2003, of unique classifications of each Census Block Group, and the unique PRIZM code for each respondent from the BES Household Telephone Survey, 2003. The GDT database is preferred and used because

There are many useful strategies for preparing GIS data for Next Generation 9-1-1. One step of preparation is making sure that all of the required fields exist (and sometimes populated) before loading into the system. While some localities add needed fields to their local data, others use an extract, transform, and load process to transform their local data into a Next Generation 9-1-1 GIS data model, and still others may do a combination of both.There are several strategies and considerations when loading data into a Next Generation 9-1-1 GIS data model. The best place to start is using a GIS data model schema template, or an empty file with the needed data layout to which you can append your data. Here are some resources to help you out. 1) The National Emergency Number Association (NENA) has a GIS template available on the Next Generation 9-1-1 GIS Data Model Page.2) The NENA GIS Data Model template uses a WGS84 coordinate system and pre-builds many domains. The slides from the Virginia NG9-1-1 User Group meeting in May 2021 explain these elements and offer some tips and suggestions for working with them. There are also some tips on using field calculator. Click the "open" button at the top right of this screen or here to view this information.3) VGIN adapted the NENA GIS Data Model into versions for Virginia State Plane North and Virginia State Plane South, as Virginia recommends uploading in your local coordinates and having the upload tools consistently transform your data to the WGS84 (4326) parameters required by the Next Generation 9-1-1 system. These customized versions only include the Site Structure Address Point and Street Centerlines feature classes. Address Point domains are set for address number, state, and country. Street Centerline domains are set for address ranges, parity, one way, state, and country. 4) A sample extract, transform, and load (ETL) for NG9-1-1 Upload script is available here.Additional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.

The Residential Schools Locations Dataset in shapefile format contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this data set, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The data set was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this data set,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School. When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. The geographic coordinate system for this dataset is WGS 1984. The data in shapefile format [IRS_locations.zip] can be viewed and mapped in a Geographic Information System software. Detailed metadata in xml format is available as part of the data in shapefile format. In addition, the field name descriptions (IRS_locfields.csv) and the detailed locations descriptions (IRS_locdescription.csv) should be used alongside the data in shapefile format.

Soil_Samples_BACI Available only by request on a case by case basis. Contact rthe author, David Nowak, at dnowak@fs.fed.us Tags Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE Summary Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE). Description The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces. Credits Rich Pouyat, USDA Forest Service Use limitations Not for profit use only Extent West -76.711030 East -76.530612 North 39.371355 South 39.200686 Scale Range There is no scale range for this item. The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

Public Use Microdata AreaThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Public Use Microdata Areas (PUMAs) in the United States. Per USCB, "nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name.”Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Public Use Microdata Areas) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, Series Information for the 2010 Census Public Use Microdata Area (PUMA) State-basedGeoplatform: TIGER/Line Shapefile, 2019, Series Information for the 2010 Census Public Use Microdata Area (PUMA) State-basedFor more information, please visit: Public Use Microdata Areas (PUMAs)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."To access other NGDA content that may interest you: NGDA Content

Long term sampling framework for the Baltimore MSA comprised of contiguous 100 meter grid cells. Used for: telephone survey, field observation survey (observational and photo data), and key informant photo-documentation (text / narrative and photo data). A unique ID, 'GridCell', is used to establish the relationship between this layer and the field data.

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


   The geodatabase itself is available online at beslter.org or lternet.edu. It is considerably large. Upon request, it can be shipped to you on media, such as a flash 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.

Ferromanganese crusts in the world's oceans may serve as potential sources of metals, such as cobalt and magnesium, valuable to civilian and military industry; these are metals that the United States would otherwise be dependent on foreign sources. Unlike abyssal ferromanganese nodules, which form in areas of low disturbance and high sediment accumulation, ferromanganese crusts have been found to contain three to five times more cobalt than abyssal ferromanganese nodules and can be found on harder, steeper substrates than abyssal plains, which can be too steep for permanent sediment accumulation. Ferromanganese crusts have also been documented on seamounts and plateaus within the U.S. exclusive economic zone in the Pacific and Atlantic Oceans and are therefore of strategic importance to the United States Government as well as to civilian mining and metallurgical industries. A database containing ferromanganese crust occurrences throughout the world's oceans was assembled from published and unpublished sources to provide data gathering and analytical information for these samples. These data provide the digital formatted locations of the sample locations of the U.S. Geological Survey and Scripps Institution Nodule Data Bank (SNDB) from appendixes A and B. These locations from 1986 and earlier are also represented on the maps of Lane and others (1986). > Lane, C.M., Manheim, F.T., Hathaway, J.C., and Ling, T.H., 1986, Station maps of the world ocean-ferromanganese-rust database: U.S. Geological Survey Miscellaneous Field Studies Map, 1869, http://pubs.usgs.gov/mf/1986/1869/

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

This shapefile is based on a seabed sediment sample database of samples collected by INSS, INFOMAR and related surveys, including ADFish, DCU, FEAS, GATEWAYS, IMAGIN, IMES, INIS_HYRDO, JIBS, MESH, SCALLOP, SEAI, SEI, UCC. Where available, the results of particle size analysis are presented by displaying percentages of mud, sand and gravel fraction. Shapefile showing location of 4185 samples and samples can be colour coded in accordance with Folk sediment type classification for samples with available PSA (Particle Size Analysis) data. Shapefile showing location of 4185 samples and Folk classification for samples with available PSA (Particle Size Analysis) data.

Coweeta LTER researchers sampled fifty-eight stream sites in the Upper Little Tennessee River Basin in February and June of 2009. Sites were selected to represent the range of land cover and land use within the basin. Samples were taken over three days of stable weather and discharge during periods of baseflow. They were used to characterize conditions across the basin during the growing and the non-growing seasons without the influence of elevated discharge. The GIS data presented was used to both help in selecting the 58 sites.

Water quality in the Barnegat Bay-Little Egg Harbor estuary along the New Jersey coast is the focus of a multidisciplinary research project begun in 2011 by the U.S. Geological Survey (USGS) in partnership with the New Jersey Department of Environmental Protection. This narrow estuary is the drainage for the Barnegat Watershed and flushed by just three inlets connecting it to the Atlantic Ocean, is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen -depletion events, seaweed, stinging nettles, and brown tide. The scale of the estuary and the scope of the problems within it necessitate a multidisciplinary approach that includes characterizing its physical characteristics (for example, depth, magnitude and direction of tidal currents, distribution of seafloor and subseafloor sediment) and modeling how the physical characteristics interact to affect the estuary's water quality. Scientists from USGS Coastal and Marine Geology Program offices in Woods Hole, Massachusetts, and St. Petersburg, Florida, began mapping the seafloor of the Barnegat Bay-Little Egg Harbor estuary in November 2011 and completed in September 2013. With funding from the New Jersey Department of Environmental Protection and logistical support from the USGS New Jersey Water Science Center, they collected data with a suite of geophysical tools, including swath bathymetric sonar for measuring seafloor depth, a sidescan sonar for collecting acoustic-backscatter data (which provides information about seafloor texture and sediment type), subbottom profiler for imaging sediment layers beneath the floor of the estuary, and sediment samples with bottom photographs for ground validation of the acoustic data. 2011-041-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2011-041-FA 2012-003-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2012-003-FA 2013-014-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-014-FA 2013-030-FA: http://woodshole.er.usgs.gov/operations/ia/public_ds_info.php?fa=2013-030-FA

The U.S. Geological Survey, in collaboration with the National Oceanic and Atmospheric Administration's (NOAA) National Marine Sanctuary Program, conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region from 1993 to 2004. The mapped area is approximately 3,700 square km (1,100 square nm) in size and was subdivided into 18 quadrangles. Several series of sea floor maps of the region based on multibeam sonar surveys have been published. In addition, 2,628 seabed sediment samples were collected and analyzed and approximately 10,600 still photographs of the seabed were acquired during the project. These data provide the basis for scientists, policymakers, and managers for understanding the complex ecosystem of the sanctuary region and for monitoring and managing its economic and natural resources.

http://www.openstreetmap.org/images/osm_logo.png" alt=""> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

This shapefile represents the offshore grid-based sampling frame intended for use with the North American Bat Monitoring Program (NABat). The grid consists of 10 km x 10 km cells spanning the oceanic waters in the Caribbean Sea.

The FDOT GIS Highway Performance Monitoring System Roadways feature class provides spatial information on features used for the sample portion of the annual HPMS submittal to the Federal Highway Administration. The HPMSIDNO denotes the 12-digit number uniquely identifying the sample section. This number cannot be changed once assigned. Even if a roadway section that contains a sample is renumbered, the HPMS ID number will remain the same. Since when created, the HPMS ID uses the first eight digits of the roadway ID, the two numbers will no longer share those eight digits in common. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 02/08/2025.For more details please review the FDOT RCI Handbook Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/hpms.zip