These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF MOBILE, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Cleaned dataset for the Pharos application 2023-2024 data collection period (May 2023-March 2024). This dataset includes the full recurring network measurement (RNM), landmark (LM) datasets, as well as the county geographies used for the study catchment area. Also included in this dataset are a text document containing the necessary requirements, as well as python script to clean and visualize the collected data replicating the methods used in our published analysis.

Various telecommunication datasets such as cellphone towers and service areas, land mobile station locations, AM, FM, and TV communication can be downloaded on an FCC page. Additionally, data files can be individually downloaded from the FCC Universal Licensing System data site. This data resource is intended to guide users toward the authoritative data source and to demonstrate at least one translation of that data into a spatial format.

The metadata for this translated dataset is here:

Antenna Structure Registration: antenna_structure_registration_mn.html

In addition, the Department of Homeland Security's Homeland Infrastructure Foundation - Level Data (HIFLD) program has an "Open Data" site, which includes a nationwide dataset on Cellular Towers derived from the FCC Universal Licensing System Database: https://hifld-geoplatform.opendata.arcgis.com/datasets/cellular-towers

Detroit Street View (DSV) is an urban remote sensing program run by the Enterprise Geographic Information Systems (EGIS) Team within the Department of Innovation and Technology at the City of Detroit. The mission of Detroit Street View is ‘To continuously observe and document Detroit’s changing physical environment through remote sensing, resulting in freely available foundational data that empowers effective city operations, informed decision making, awareness, and innovation.’ LiDAR (as well as panoramic imagery) is collected using a vehicle-mounted mobile mapping system.

Due to variations in processing, index lines are not currently available for all existing LiDAR datasets, including all data collected before September 2020. Index lines represent the approximate path of the vehicle within the time extent of the given LiDAR file. The actual geographic extent of the LiDAR point cloud varies dependent on line-of-sight.

Compressed (LAZ format) point cloud files may be requested by emailing gis@detroitmi.gov with a description of the desired geographic area, any specific dates/file names, and an explanation of interest and/or intended use. Requests will be filled at the discretion and availability of the Enterprise GIS Team. Deliverable file size limitations may apply and requestors may be asked to provide their own online location or physical media for transfer.

LiDAR was collected using an uncalibrated Trimble MX2 mobile mapping system. The data is not quality controlled, and no accuracy assessment is provided or implied. Results are known to vary significantly. Users should exercise caution and conduct their own comprehensive suitability assessments before requesting and applying this data.

Sample Dataset: https://detroitmi.maps.arcgis.com/home/item.html?id=69853441d944442f9e79199b57f26fe3

Land mobile broadcast locations in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

Mobile Home Park Locations

Locations and details of mobile home parks in the United States

By Homeland Infrastructure Foundation [source]

About this dataset

The Mobile Home Parks Inventory dataset provides a comprehensive list of mobile home parks across the United States. This dataset is crucial for emergency preparedness and evacuation planning, as mobile home parks are inhabited by a vulnerable population that is particularly susceptible to natural disasters such as hurricanes, tornadoes, and flooding.

The dataset includes detailed information about each mobile home park, including its location coordinates (longitude and latitude), address details (street address, city, state, ZIP code), and additional address information if available. It also provides contact details such as telephone numbers and websites for further information about each park.

Furthermore, the dataset contains essential attributes related to the characteristics of mobile home parks. These attributes include the number of units (mobile homes) within each park, allowing authorities to assess capacity during emergency situations. Additionally, it categorizes the type of each park (e.g., recreational vehicle parks), its status (e.g., operational or closed), and its size classification.

To ensure data accuracy and reliability, various validation methods have been implemented. The validation process includes verifying the data sources from where this information was obtained along with dates when data was sourced or validated.

Moreover, this comprehensive inventory incorporates geographical references with FIPS codes for counties in which these mobile home parks are located. Furthermore,the NAICS code provides an additional industry classification system describing these facilities in greater detail.

Lastly,this Mobile Home Parks Inventory recognizes that reverse geocoding has been employed for gathering precise spatial coordinates.Because vulnerability differs across regions,state boundaries have also been included to facilitate analysis at a higher level.Alongside state boundaries,this dataset acknowledges country-level variations which could be valuable while comparing international mobile homes inventories .

By utilizing this extensive collection of accurate and up-to-date information on mobile home parks in the United States policymakers,government agencies,and emergency responders can effectively plan evacuation strategies,mobile resource allocation,and disaster response efforts for ensuring public safety during natural calamities.This valuable knowledge will ultimately enhance disaster mitigation and the overall resilience of these vulnerable communities

How to use the dataset

• Understanding the Columns:

  • X and Y: These columns represent the longitude and latitude coordinates of each mobile home park. They can be used for geographical analysis and mapping purposes.
  • NAME: This column provides the name of each mobile home park. It is useful for identifying specific parks.
  • ADDRESS: The street address where each mobile home park is located.
  • ADDRESS2: Additional address information (if available) for each mobile home park.
  • CITY: The city where each mobile home park is situated.
  • STATE: The state where each mobile home park is located.
  • ZIP and ZIP4: These columns contain the ZIP code information for each mobile home park, including additional ZIP code details if available.
  • TELEPHONE: The contact telephone number for each mobile home park, which can be useful for making inquiries or gathering more information directly from them.
  • TYPE: This column indicates the type of the mobile home park (e.g., permanent residential, seasonal).
  • STATUS: The status of a particular mobile home park (e.g., open, closed).
  • COUNTY and COUNTYFIPS:The county where each mobile h0me_1park is situated along with its associated FIPS code.

• Analyzing Park Characteristics: UNITS & SIZE columns provide insights into various aspects: UNITS represents the number of individual dwelling units within a given Mobile Home Park SIZE describes its physical size.

• Demographic Analysis: By referring to NAICS_CODE & NAICS_DESC columns ,you'll get an idea about the associated industries and business activities in the vicinity of each park.

• Geographical Analysis: The LATITUDE and LONGITUDE coordinates allow you to map out mobile home parks on various GIS (Geographic Information System) platforms. You can analyze the distribution of mobile home parks across different states, cities, or counties.

• Emergency Preparedness: ...

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay, Alabama suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Cellular Phone Towers dataset current as of 2007. Serve as base information for use in GIS systems for general planning, analytical, and research purposes..

This dataset represents Land Mobile Commercial transmission tower locations as recorded by the Federal Communications Commission, extracted from the FCC Licensing Database. This serves as base information for use in GIS systems for general planning, analytical, and research purposes. It is not intended for engineering work or to legally define FCC licensee data or FCC market boundaries. The material in these data and text files are provided as-is. The FCC disclaims all warranties with regard to the contents of these files, including their fitness. In no event shall the FCC be liable for any special, indirect, or consequential damages whatsoever resulting from loss or use, data or profits, whether in connection with the use or performance of the contents of these files, action of contract, negligence, or other action arising out of, or in connection with the use of the contents of these files. It is know that there are some errors in the licensing information - Latitude, Longitude and Ground Elevation data as well as frequency assignment data from which these files were generated.

Building feature updates include Buildings (all types), trailers overhead structures, and docks/piers. This dataset is provided as part of 1":100' planimetric update mapping of bridge, buildings, driveways, parking, edge of pavement, and sidewalk features. Updates were performed by Kucera International, Inc. in 2023-2024 for approximately 472 square miles, including the following areas: City of Mobile metropolitan area, the Big Creek Watershed, the City of Chickasaw, and portions of the Cities of Prichard, Saraland, and Satsuma. See 2023 update boundary or contact City of Mobile for more information. Using the YEAR_REVIEWED field will tell you which ortho image year the feature was updated. The updates are based on three inch resolution RGB ortho-rectified aerial imagery that was collected in 2022 for the western half of Mobile County and in 2023 for the eastern half of Mobile County. Features were manually updated using “heads-up” fashion digitizing at a 1” = 100’ scale in 2D using the 3-inch resolution ortho-rectified imagery. Features that are new, no longer present or of significant change (defined by 10' by 10') are updated as they appear in 2022/2023 imagery. Unpaved parking added in 2023/2024 are within commercial and industrial parcels, as well as other areas specified as per case by the City of Mobile. The following SURFACE_TYPE attribute domain was used for features added in 2023/2024, unless otherwise specified as per case by City of Mobile: PAVED, UNPAVED. Other various surface types in the dataset were unchanged or modified in 2023/2024.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

These data provide an accurate high-resolution shoreline compiled from imagery of WESTERN MOBILE BAY, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

These data were automated to provide an accurate high-resolution historical shoreline of Mobile Bay Entrance, AL suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS a...

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

This dataset provides census-block level estimates of outdoor air pollutant concentrations averaged over a 32-month period spanning from May 2015 through December 2017. Measurements were taken using mobile monitoring along every street of 13 cities, towns, and urban districts (93 km2) distributed through four counties of the San Francisco Bay Area, comprising over 2,100 hours of sampling. Two Google Street View cars equipped with the Aclima mobile platform repeatedly measured city block air quality, providing estimates of outdoor air pollution for a year-2010 population of ~450,000 individuals. This dataset includes measurements of four pollutants:NO (units: ppb)NO2 (units:ppb)BC (black carbon, units: µg/m3)UFP (ultrafine particle count, units: #x103/cm3)For the purposes of quality control, the set includes:a count of the unique days each block was visited while each of the monitoring instruments were operational (uniqueDays_xx, with xx representing the relevant pollutant), the total visits to the census block (visits_xx), and the cumulative sampling time in seconds (samplingTime_xx).The dataset includes the population data from the US census (TotalPop.x) and populations divided by census-based self-identified race and ethnicity. The categories include:-Hispanic or Latino (HispLat)-Not Hispanic or Latino: White alone or in combination with one or more other races (WhiteNH)-Not Hispanic or Latino: Black or African American alone or in combination with one or more other races (BlackNH)-Not Hispanic or Latino: Asian alone or in combination with one or more other races (AsianNH)-Not Hispanic or Latino: American Indian and Alaska Native alone or in combination with one or more other races (NativeNH)-Not Hispanic or Latino: Native Hawaiian and Other Pacific Islander alone or in combination with one or more other races (PacIsl)-Not Hispanic or Latino: Some Other Race alone or in combination with one or more other races (OtherNH)Consistent with Chambliss et al. 2021, there is a grouping of "OtherRace" which is the sum of the last three categories.Additional methodological details can be found in Chambliss et al. 2021 (https://chemrxiv.org/engage/chemrxiv/article-details/60f731b1880443777ae27104).Data are provided as a table that may be joined to GIS data from the US census using the unique "GISJOIN" identifier matching 2010 census block geographic units. Such GIS data is available online from several sources, including https://www.nhgis.org/

The U.S. Geological Survey (USGS) Lower Mississippi-Gulf Water Science Center (LMGWSC) in collaboration with the U.S. Environmental Protection Agency and funded through the Resources and Ecosystems, Sustainability, Tourist, Opportunities, and Revived Economies of the Gulf Coast States Act (RESTORE Act) are conducting a multiyear multistate study to analyze the alteration and trends of streamflow delivery to the Gulf of Mexico; the results of of which are used in an OASIS model decision support tool (Hazen and Sawyer, New York, NY, USA). To fully develop the OASIS model, a geospatial polyline boundary delineating the Mobile and Perdido bays OASIS streamflow prediction model study area was created. The spatial extent of this polyline includes the upstream contributing drainage area of Mobile and Perdido bays, encompassing Mobile-Tombigbee (HUC04 0316), Alabama (HUC04 0315), Perdido (HUC08 03140107), and Perdido Bay (HUC08 03140106) watersheds. Another geospatial polyline is provided and is a buffer around the study area. The buffer is bounded by the drainage divide of the Mississippi River to the west, the drainage divide of the Apalachicola-Chattahoochee-Flint River System to the east, and the drainage divide of the Tennessee River to the north. The purpose of the buffer is to provide a geographic extent larger than the study area that can be used to filter data used in regional modeling. Having training data adjacent to the study area can limit error associated with regional modeling at the geographic edges of the study area. The statistical buffer spatial extent was delineated using well-known geographic information system tools and the National Hydrography Dataset Hydrologic Unit Code geospatial files and other infrastructure.

These data provide an accurate high-resolution shoreline compiled from imagery of Upper Mobile and Tensaw Rivers, AL . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Land mobile commercial towers in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.