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.

This map is just one of the many data visualizations on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

It is classified into map API, data API, and mobile SDK, and it is a service that provides data and map service of population, household, housing, and business owned by Statistics Korea so that other organizations and services can use it. ○ Map API: Provides API for map service provided by SGIS Open Platform ○ Data API: Provides API to use data on population, household, housing, business, etc. owned by Statistics Korea ○ Mobile SDK: Map based on Android and iOS SDK provided in native language to develop services

The USGS Topo base map service from The National Map is a combination of contours, shaded relief, woodland and urban tint, along with vector layers, such as geographic names, governmental unit boundaries, hydrography, structures, and transportation, to provide a composite topographic base map. Data sources are the National Atlas for small scales, and The National Map for medium to large scales.

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

The Cancer Mapping data consists of counts of newly diagnosed cancer among New York State residents and is in response to legislation regarding "Cancer incidence and environmental facility maps" signed into law in 2010 (Public Health Law §2401-B). The law specifies the publication of maps showing cancer counts for small geographic areas along with certain facilities regulated by the State Department of Environmental Conservation. The official web site is called Environmental Facilities and Cancer Mapping.

The dataset is ONLY for the cancer-related data fields on the Environmental Facilities and Cancer Mapping web site. This dataset includes observed counts for 23 separate anatomical sites at the level of census block group. Block groups are small geographic areas typically averaging 1,000 to 1,500 people. To protect confidentiality, each area contains a minimum of 6 total cancers among males and 6 total cancers among females.

For more information, check out http://www.health.ny.gov/statistics/cancer/registry/about.htm .

The Digital Geologic Map of the U.S. Geological Survey Mapping in the Western Portion of Amistad National Recreation Area, Texas is composed of GIS data layers complete with ArcMap 9.3 layer (.LYR) files, two ancillary GIS tables, a Map PDF document with ancillary map text, figures and tables, a FGDC metadata record and a 9.3 ArcMap (.MXD) Document that displays the digital map in 9.3 ArcGIS. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Eddie Collins, Amanda Masterson and Tom Tremblay (Texas Bureau of Economic Geology); Rick Page (U.S. Geological Survey); Gilbert Anaya (International Boundary and Water Commission). Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation sections(s) of this metadata record (wpam_metadata.txt; available at http://nrdata.nps.gov/amis/nrdata/geology/gis/wpam_metadata.xml). All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data is available as a 9.3 personal geodatabase (wpam_geology.mdb), and as shapefile (.SHP) and DBASEIV (.DBF) table files. The GIS data projection is NAD83, UTM Zone 14N. The data is within the area of interest of Amistad National Recreation Area.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

This layer contains the latest 14 months of unemployment statistics from the U.S. Bureau of Labor Statistics (BLS). The data is offered at the nationwide, state, and county geography levels. Puerto Rico is included. These are not seasonally adjusted values.The layer is updated monthly with the newest unemployment statistics available from BLS. There are attributes in the layer that specify which month is associated to each statistic. Most current month: April 2025 (preliminary values at the county level)The attributes included for each month are:Unemployment rate (%)Count of unemployed populationCount of employed population in the labor forceCount of people in the labor forceData obtained from the U.S. Bureau of Labor Statistics. Data downloaded: June 24th, 2025Local Area Unemployment Statistics table download: https://www.bls.gov/lau/#tablesLocal Area Unemployment FTP downloads:State and CountyNationData Notes:This layer is updated automatically when the BLS releases their most current monthly statistics. The layer always contains the most recent estimates. It is updated within days of the BLS's county release schedule. BLS releases their county statistics roughly 2 months after-the-fact. The data is joined to 2023 TIGER boundaries from the U.S. Census Bureau.Monthly values are subject to revision over time.For national values, employed plus unemployed may not sum to total labor force due to rounding.As of the January 2022 estimates released on March 18th, 2022, BLS is reporting new data for the two new census areas in Alaska - Copper River and Chugach - and historical data for the previous census area - Valdez Cordova.As of the March 17th, 2025 release, BLS now reports data for 9 planning regions in Connecticut rather than the 8 previous counties.To better understand the different labor force statistics included in this map, see the diagram below from BLS:

National and second-level release area map for each county and city (The Ministry of the Interior's Statistics Department has established a "Statistical Area Classification System," which includes the smallest statistical area, first-level release area, and second-level release area, as a concept of a small statistical area. Several smallest statistical areas make up a first-level release area, and several first-level release areas further make up a second-level release area, and so on, to establish a spatial unit system for the statistical release of Taiwan's socio-economic data.)

Taichung City spatial map query administrative district statistical files in January 2011

The National Transit Map - Stops dataset was compiled on March 19, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Transit Map (NTM) is a nationwide catalog of fixed-guideway and fixed-route transit service in America. It is compiled using General Transit Feed Specification (GTFS) Schedule data. The NTM Stops dataset shows stops where vehicles pick up or drop off riders. This dataset uses the GTFS stops.txt file. The GTFS schedule format and structure documentation is available at, https://gtfs.org/schedule/. To improve the spatial accuracy of the NTM Stops, the Bureau of Transportation Statistics (BTS) adjusts transit stops using context from the submitted GTFS source data and/or from other publicly available information about the transit service. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529049

This analysis uses location data collected on elk that were fitted with GPS collars in Idaho for 2007 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual elk spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a less than 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers.South Fork of the Payette River Elk Migration StatisticsAnalyzed/Prepared by: Jodi Berg and Scott BergenFebruary 2021Spatial MetricsAverage length of Migration: 29.0 milesMaximum Migration Length: 113.8 milesMinimum Migration Length: 3.0 milesTotal Migrations Analyzed: 547Total Number of Individuals: 218Total Number Spring Migrations: 338Total Number Fall Migrations: 209Of 547 individual seasonal migrations, 188 used Brownian bridge movement models with an 8-hour time-lag, 141 used forced motion variance (1400 m) models with a 14-hour time-lag, and 218 used force motion variance (1400 m) models with a 27-hour time-lag.Temporal Data Extent of Study: April 4, 2008 – February 16, 2020Spring MigrationFall MigrationStart Date AverageApril 28October 26 Minimum February 1July 19 MaximumAugust 19March 6End Date AverageMay 24November 30 MinimumFebruary 23September 3 MaximumAugust 21March 17Duration Average2735 Minimum21 Maximum144137Migration Use Class StatisticsUse ClassAcres 1 individual1,555,266 Low (>2 individuals)685,624 Medium (10-20%)86,901 High (>20%)23,557 Stopover90,940

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

For many people data is seen as abstract information. It is therefore valuable to use Matrixian Map, an interactive map that shows an enormous amount of data in one figure. It helps to make complex analyzes understandable, to see new opportunities and to make data-driven decisions.

With our large amount of consumer, real estate, mobility and logistics data we can design very extensive maps. Whether it concerns a map that shows your (potential) customers, shows on which roofs solar panels can be placed or indicates when shopping areas can be supplied, with our knowledge of households, companies and objects, almost anything is possible!

Lat long data for sampling locations.

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

Esri ArcGIS Online (AGOL) Feature Layer which provides access to the MDOT SHA County Flood Statistics MapsMDOT SHA County Flood Statistics Maps data consists of polygon geometric features which represent the geographic extent of each Maryland County with an available MDOT SHA County Flood Statistics Map. Users of this layer should consume the URL contained within each pop-up to access the MDOT SHA County Flood Statistics Map.MDOT SHA County Flood Statistics Maps data is owned & maintained by the MDOT SHA OPPE Innovative Planning & Performance Division (IPPD).For more information related to the maps, contact MDOT SHA OPPE Innovative Planning & Performance Division (IPPD):Email: IPPD@mdot.maryland.govFor more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This analysis uses location data collected on mule deer that were fitted with GPS collars in Idaho for 2003 – 2019. Individuals using a winter range (as defined as a winter herd), were used for the analysis if their location data was available at the time of the analysis. Each individual’s location dataset is used to estimate winter and summer ranges, and seasonal spring and fall migration using net-squared displacement techniques (Bunnefeld et al. 2011). Fall and spring migration locations are used for the migration route analysis. After individual mule deer spring and fall migration locations are determined, a Brownian Bridge Movement Model (BBMM, Horne et al. 2007) is used to estimate the individuals Utilized Distribution (UD) during the seasonal migrations. Depending of the frequency of the location data, either a BBMM or a Forced Motion Variance model (FMV) are used as an estimate of that season’s migration UD. If locations collected at a < 7hr schedule, the migration used BBMM modeling techniques. If the schedule was greater than 7 hrs a FMV modeling technique was used (Fatteberge et al, in review). Further, FMV techniques that allowed for a 14 hour gap in location schedule were preferred over FMV models that used a maximum of 27 hr gap. When an individual had several seasonal migrations, the resulting UDs distributions are combined and averaged to create a single UD of all the seasonal migrations conducted by that individual. Individual UDS are then combined for all individuals in the winter herd with available UD information. For migration routes, the following classes were delineated based on the area’s use across the winter herd, used by 1 individual, used by 2individuals to 10% of the winter herd, 10 to 20% use of the winter herd, and greater than 20% use by the winter herd. The combined individual UDS are aggregated to estimate winter herd stopover locations. From the combined winter herd UD, the top 10% of recorded values are selected to represent population level stopovers. Sand Creek Mule Deer Migration StatisticsAnalyzed/Prepared by: Jodi Berg and Scott BergenMay 2021Spatial MetricsAverage length of Migration: 35.3 milesMaximum Migration Length: 74.4 milesMinimum Migration Length: 11.5 milesTotal Migrations Analyzed: 56Total Number of Individuals: 24Total Number Spring Migrations: 34Total Number Fall Migrations: 22Of 56 individual seasonal migrations, 9 used Brownian bridge movement models with an 8-hour time-lag, 9 used forced motion variance (1400 m) models with a 14-hour time-lag, and 38 used forced motion variance (1400 m) models with a 27-hour time-lag.Temporal Data Extent of Study: April 16, 2013 – June 5, 2019Spring MigrationFall MigrationStart Date AverageApril 1October 29 Minimum February 16September 27 MaximumMay 3December 1End Date AverageMay 7December 1 MinimumMarch 12October 24 MaximumJune 7December 22Duration Average3325 Minimum33 Maximum7777Migration Use Class StatisticsUse ClassAcres 1 individual565,658 Low (>2 individuals)279,661 Medium (10-20%)143,739 High (>20%)56,150 Stopover30,885

Cook County Department of Public Health Suburban Cook County District Map