The SACS study area is subdivided into 22 planning reaches (Figure 4 1) derived from three datasets and visual edits based on coastal geomorphology and professional judgment. Datasets include the following:- The Nature Conservancy Ecoregions—boundaries of areas that The Nature Conservancy has prioritized for conservation- State boundaries- Maximum inland limit of Category 5 storm surge inundation represented by the NOAA Sea, Lake, and Overland Surges from Hurricanes (SLOSH) modelThe GIS process to develop the Planning Reaches entailed the follow:The most landward extent of the SLOSH model was manually measured. Based on that measurement a single sided buffer was generated contiguous to the Coast for the AOR. The buffer was manually edited to include some areas that fell outside the buffer distance, specifically in Northern North Carolina and around Mobile Alabama. The Union tool was then used in ArcGIS desktop to overlay Ecoregions and State boundary files. Then the intersect tool was used to overlay the SLOSH buffer with the Union file. The result of the Intersect was then manually cut along the lines defined by the coastal geomorphology using lines defined in the “Manual_Edit_lines” feature. The resulting feature class was then provided with names based on the state two-digit acronym and a sequential number.

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

Environmental Justice 2024 Set is comprised of two layers: Environmental Justice Block Groups 2024 and Environmental Justice Distressed Municipality 2024. All Census and ACS data used in the creation of these data are the latest available from the Census at time of calculation. Environmental Justice Block Groups 2024 was created from Connecticut block group boundary data located in the Census Bureau's 2024 Block Group TIGER/Line Shapefiles. The poverty data used to determine which block groups qualified as EJ communities (see CT State statute 22a-20a) was based on the Census Bureau's 2023 ACS 5-year estimate. This poverty data was joined with the block group boundaries in ArcPro. Block groups in which the percent of the population below 200% of the federal poverty level was greater than or equal to 30.0 were selected and the resulting selection was exported as a new shapefile. The block groups were then clipped so that only those block groups outside of distressed municipalities were displayed. Maintenance – This layer will be updated annually and will coincide with the annual distressed municipalities update (around August/September). The latest ACS 5-year estimate data should be used to update this layer. Environmental Justice Distressed Municipalities 2024 was created from the Connecticut town boundary data located in the Census Bureau's 2024 TIGER/Line Shapefiles (County Subdivisions). From this shapefile, "select by attribute" was used to select the distressed municipalities by town name (note: the list of 2024 distressed municipalities was provided by the CT Department of Economic and Community Development). The selection was then exported a new shapefile. The “Union” tool was used to unite the new shapefile with tribal lands (American Indian Area Geography) boundary data from the 2024 TIGER/Line files. In the resulting layer, the tribal lands were deleted so only the distressed municipalities remained. Maintenance – This layer will be updated

The Japan Geospatial Analytics market is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 13.95% from 2019 to 2033. With a market size of $1.53 billion in 2025, the sector is driven by increasing adoption of advanced technologies like AI and machine learning within various end-user verticals. Specifically, the robust growth is fueled by expanding applications in the agricultural sector for precision farming, optimizing resource allocation, and improving crop yields. The utility and communication sectors leverage geospatial analytics for network planning, infrastructure management, and disaster response. The defense and intelligence sectors utilize this technology for surveillance, mapping, and strategic decision-making, further stimulating market growth. Government initiatives promoting smart city development and digitalization contribute significantly, along with increasing adoption within mining, transportation, and real estate sectors for enhanced efficiency and risk mitigation. Despite the positive outlook, the market faces certain challenges. Data security concerns and the need for skilled professionals to manage and interpret complex geospatial data represent key restraints. However, ongoing advancements in technology, coupled with increasing government investment in infrastructure projects and digital transformation, are expected to mitigate these challenges and propel continued market expansion throughout the forecast period. The market segmentation reveals robust growth across diverse types, including surface analysis, network analysis, and geovisualization, each catering to specific needs and applications within the aforementioned end-user verticals. Major players like Esri, Hexagon AB, and Trimble are actively shaping the market landscape through continuous innovation and strategic partnerships. The rising demand for location intelligence and the development of sophisticated analytical tools further suggest the Japan Geospatial Analytics market will maintain its upward trajectory in the coming years. Recent developments include: April 2024: Microsoft announced a significant investment of USD 2.9 billion over the next two years to enhance its hyperscale cloud computing and AI infrastructure in Japan. The company will also expand its digital skilling programs to provide AI training to over 3 million individuals within the next three years., May 2024: The European Union and Japan began their Digital Partnership, reviewing the progress made since the first Digital Partnership Council in 2023. The partners agreed on new deliverables to enhance cooperation on critical digital technologies. These include artificial intelligence (AI), 5G to 6G advancements, semiconductors, high-performance computing (HPC), quantum technology, submarine cables, eID, and cybersecurity., *These technological advancements are poised to drive the evolution of geospatial analytics tools and technologies.. Key drivers for this market are: Increase In Adoption of Smart City Development, Introduction of 5G to Boost Market Growth. Potential restraints include: Increase In Adoption of Smart City Development, Introduction of 5G to Boost Market Growth. Notable trends are: Disaster Risk Reduction and Management.

Environmental Justice Block Groups 2022 was created from Connecticut block group boundary data located in the Census Bureau's 2020 TIGER/Line Shapefiles. The poverty data used to determine which block groups qualified as EJ communities (see CT State statute 22a-20a) was based on the Census Bureau's 2020 ACS 5-year estimate. This poverty data was joined with the block group boundaries in ArcPro. Block groups in which the percent of the population below 200% of the federal poverty level was greater than or equal to 30.0 were selected and the resulting selection was exported as a new shapefile. The block groups were then clipped so that only those block groups outside of distressed municipalities were displayed. Maintenance – This layer will be updated annually and will coincide with the annual distressed municipalities update (around August/September). The latest ACS 5-year estimate data should be used to update this layer. Environmental Justice Distressed Municipalities 2020 was created from Connecticut town boundary data located in the Census Bureau's 2020 TIGER/Line Shapefiles (County Subdivisions).

From this shapefile, "select by attribute" was used to select the distressed municipalities by town name (note: the list of 2022 distressed municipalities was provided by the CT Department of Economic and Community Development). The selection was then exported a new shapefile. The “Union” tool was used to unite the new shapefile with tribal lands (American Indian Area Geography) boundary data from the 2020 TIGER/Line files. In the resulting layer, the tribal lands were deleted so only the distressed municipalities remained. Maintenance – This layer will be updated annually when the DECD produces its new list of distressed municipalities (around August/September).

Note: A distressed municipality, as designated by the Connecticut Department of Economic and Community Development, includes municipalities that no longer meet the threshold requirements but are still in a 5-year grace period. (See definition at CGS Sec. 32-9p(b).) Fitting into that grace period, eight towns continue to be eligible for distressed municipality benefits because they dropped off the list within the last five years. Those are Enfield, Killingly, Naugatuck, Plymouth, New Haven, Preston, Stratford, and Voluntown.

de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San Miguel-Ayanz, J., 2013. Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling. Geophysical Research Abstracts 15, 13245+. ISSN 1607-7962, European Geosciences Union (EGU).

This is the authors’ version of the work. The definitive version is published in the Vol. 15 of Geophysical Research Abstracts (ISSN 1607-7962) and presented at the European Geosciences Union (EGU) General Assembly 2013, Vienna, Austria, 07-12 April 2013http://www.egu2013.eu/

Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling

Daniele de Rigo ¹ ², Paolo Corti ¹ ³, Giovanni Caudullo ¹, Daniel McInerney ¹, Margherita Di Leo ¹, Jesús San-Miguel-Ayanz ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy ² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy ³ United Nations World Food Programme,Via C.G.Viola 68 Parco dei Medici, I-00148 Rome, Italy

Excerpt: Interfacing science and policy raises challenging issues when large spatial-scale (regional, continental, global) environmental problems need transdisciplinary integration within a context of modelling complexity and multiple sources of uncertainty. This is characteristic of science-based support for environmental policy at European scale, and key aspects have also long been investigated by European Commission transnational research. Approaches (either of computational science or of policy-making) suitable at a given domain-specific scale may not be appropriate for wide-scale transdisciplinary modelling for environment (WSTMe) and corresponding policy-making. In WSTMe, the characteristic heterogeneity of available spatial information and complexity of the required data-transformation modelling (D-TM) appeal for a paradigm shift in how computational science supports such peculiarly extensive integration processes. In particular, emerging wide-scale integration requirements of typical currently available domain-specific modelling strategies may include increased robustness and scalability along with enhanced transparency and reproducibility. This challenging shift toward open data and reproducible research (open science) is also strongly suggested by the potential - sometimes neglected - huge impact of cascading effects of errors within the impressively growing interconnection among domain-specific computational models and frameworks. Concise array-based mathematical formulation and implementation (with array programming tools) have proved helpful in supporting and mitigating the complexity of WSTMe when complemented with generalized modularization and terse array-oriented semantic constraints. This defines the paradigm of Semantic Array Programming (SemAP) where semantic transparency also implies free software use (although black-boxes - e.g. legacy code - might easily be semantically interfaced). A new approach for WSTMe has emerged by formalizing unorganized best practices and experience-driven informal patterns. The approach introduces a lightweight (non-intrusive) integration of SemAP and geospatial tools - called Geospatial Semantic Array Programming (GeoSemAP). GeoSemAP exploits the joint semantics provided by SemAP and geospatial tools to split a complex D-TM into logical blocks which are easier to check by means of mathematical array-based and geospatial constraints. Those constraints take the form of precondition, invariant and postcondition semantic checks. This way, even complex WSTMe may be described as the composition of simpler GeoSemAP blocks. GeoSemAP allows intermediate data and information layers to be more easily and formally semantically described so as to increase fault-tolerance, transparency and reproducibility of WSTMe. This might also help to better communicate part of the policy-relevant knowledge, often diffcult to transfer from technical WSTMe to the science-policy interface. [...]

This dataset includes the data used to develop Map 10 for the Connect SoCal 2024 Equity Analysis Technical Report, adopted on April 4, 2024. The dataset includes binary indicators for seven climate hazard geographies in the SCAG region based on the latest available geographic information. The nine layers were overlaid using the ArcGIS union tool with a minimum of one and a maximum of six overlapping layers in the region. The climate hazard layers include: flooding, landslides, sea-level rise, extreme heat, wildfires, drought, and earthquake hazard zones. This dataset was prepared to share more information from the maps in Connect SoCal 2024 Equity Analysis Technical Report. To reduce the loading time for the complex geometry of this layer, the vertices were simplified with a tolerance of 1 foot. For more details on the methodology, please see the methodology section(s) of the Equity Analysis Technical Report: https://scag.ca.gov/sites/main/files/file-attachments/23-2987-tr-equity-analysis-final-040424.pdf?1712261887 For more details about SCAG's models, or to request model data, please see SCAG's website: https://scag.ca.gov/data-services-requestsClimate Hazard Source Details:Flood: 100-year and 500-year flood areas from Federal Emergency Management Agency (FEMA); 2017Landslide: Landslide zones from California Geological Survey (CGS), California Department of Conservation; California Department of Conservation, California Geological Survey Geologic Maps, Accessed June 2023Sea-level rise: areas vulnerable to 1 meter of sea-level rise from the Coastal Storm Modeling System (CoSMoS), U.S. Geological Survey (USGS); Coastal Storm Modeling System (CoSMoS) for Southern California, v3.0, Phase 2, 2018, USGSWildfire: Local Responsibility Areas and State Responsibility Areas that are moderate, high, and very high risk of wildfires from CalFire; Fire Hazard Severity Zones Local Responsibility Areas Maps, 2007/2008, Wildland Urban Interface, 2020, CAL FIIREExtreme heat: areas that are projected to experience more than two heat health events from 2031 to 2050 from the California Heat Assessment Tool, California Natural Resources Agency, Accessed June 2023Drought: areas that experienced severe, extreme, or exceptional drought during September 2014 from U.S. Drought Monitor, Accessed June 2023Earthquake: Alquist-Priolo Earthquake Fault Zones from the California Geological Survey (CGS), California Department of ConservationSubstandard Housing: Housing units without plumbing facilities, U.S. Census Bureau American Communities Survey 5-year estimates 2017-2021

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.Note: A new version of this data was released November 22, 2022 and is available here. There are significant changes, see the Justice40 Initiative criteria for details.This layer assesses and identifies communities that are disadvantaged according to Justice40 Initiative criteria. Census tracts in the U.S. and its territories that meet the Version 0.1 criteria are shaded in a semi-transparent blue to work with a variety of basemaps.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 0.1 of the source data downloaded May 30, 2022.Use this layer to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications. See this blog post for more information.From the source:"Census tract geographical boundaries are determined by the U.S. Census Bureau once every ten years. This tool utilizes the census tract boundaries from 2010 because they match the datasets used in the tool. The U.S. Census Bureau will update these tract boundaries in 2020.Under the current formula, a census tract will be identified as disadvantaged in one or more categories of criteria:IF the tract is above the threshold for one or more environmental or climate indicators AND the tract is above the threshold for the socioeconomic indicatorsCommunities are identified as disadvantaged by the current version of the tool for the purposes of the Justice40 Initiative if they are located in census tracts that are at or above the combined thresholds in one or more of eight categories of criteria.The goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening toolPurpose"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40The layer has some transparency applied to allow it to work sufficiently well on top of many basemaps. For optimum map display where streets and labels are clearly shown on top of this layer, try one of the Human Geography basemaps and set transparency to 0%, as is done in this example web map.Browse the DataView the Data tab in the top right of this page to browse the data in a table and view the metadata available for each field, including field name, field alias, and a field description explaining what the field represents.

Methodology – This layer was created from Connecticut town boundary data located in the Census Bureau's 2019 TIGER/Line Shapefiles (County Subdivisions). From this shapefile, "select by attribute" was used to select the distressed municipalities by town name (note: the list of 2021 distressed municipalities was provided by the CT Department of Economic and Community Development). The selection was then exported a new shapefile. The “Union” tool was used to unite the new shapefile with tribal lands (American Indian Area Geography) boundary data from the 2019 TIGER/Line files. In the resulting layer, the tribal lands were deleted so only the distressed municipalities remained.

Environmental Justice 2023 Set is comprised of two layers: Environmental Justice Block Groups 2023 and Environmental Justice Distressed Municipality 2023. All Census and ACS data used in the creation of these data are the latest available from the Census at time of calculation. Environmental Justice Block Groups 2023 was created from Connecticut block group boundary data located in the Census Bureau's 2022 Block Group TIGER/Line Shapefiles. The poverty data used to determine which block groups qualified as EJ communities (see CT State statute 22a-20a) was based on the Census Bureau's 2021 ACS 5-year estimate. This poverty data was joined with the block group boundaries in ArcPro. Block groups in which the percent of the population below 200% of the federal poverty level was greater than or equal to 30.0 were selected and the resulting selection was exported as a new shapefile. The block groups were then clipped so that only those block groups outside of distressed municipalities were displayed. Maintenance – This layer will be updated annually and will coincide with the annual distressed municipalities update (around August/September). The latest ACS 5-year estimate data should be used to update this layer. Environmental Justice Distressed Municipalities 2023 was created from the Connecticut town boundary data located in the Census Bureau's 2022 TIGER/Line Shapefiles (County Subdivisions). From this shapefile, "select by attribute" was used to select the distressed municipalities by town name (note: the list of 2023 distressed municipalities was provided by the CT Department of Economic and Community Development). The selection was then exported a new shapefile. The “Union” tool was used to unite the new shapefile with tribal lands (American Indian Area Geography) boundary data from the 2020 TIGER/Line files. In the resulting layer, the tribal lands were deleted so only the distressed municipalities remained. Maintenance – This layer will be updated annually when the DECD produces its new list of distressed municipalities (around August/September).Note: A distressed municipality, as designated by the Connecticut Department of Economic and Community Development, includes municipalities that no longer meet the threshold requirements but are still in a 5-year grace period. (See definition at CGS Sec. 32-9p(b).) Fitting into that grace period, ten towns continue to be eligible for distressed municipality benefits because they dropped off the list within the last five years. Those are Bristol, Enfield, Groton, Killingly, Naugatuck, New Haven, North Stonington, Plainfield, Preston, and Stratford.

The Florida Department of Revenue’s Property Tax Oversight(PTO) program collects parcel level Geographic Information System (GIS) data files every April from all of Florida’s 67 county property appraisers’ offices. This GIS data was exported from these file submissions in August 2024. The GIS parcel polygon features have been joined with the real property roll (Name – Address – Legal, or NAL) file. No line work was adjusted between county boundaries, prior to the creation of the centroids.. The polygon data set represents the information property appraisers gathered from the legal description on deeds, lot layout of recorded plats, declaration of condominium documents, recorded and unrecorded surveys.The centroids were derived from the parcel polygons. Using ArcPRO, the repair geometry tool was used, followed by the feature to point tool. All centroids were created within the polygon's boundary even if the center of the irregular polygon was outside its boundary. Individual parcel data is updated continually by each county property appraiser as needed. The GIS linework and related attributions for the statewide parcel map are updated annually by the Department every August. The dataset extends countywide and is attribute by Federal Information Processing Standards (FIPS) code.DOR reference with FIPS county codes and attribution definitions - https://fgio.maps.arcgis.com/home/item.html?id=ff7b985e139c4c7ba844500053e8e185If you discover the inadvertent release of a confidential record exempt from disclosure pursuant to Chapter 119, Florida Statutes, public records laws, immediately notify the Department of Revenue at 850-717-6570 and your local Florida Property Appraisers’ Office.Please contact the county property appraiser with any parcel specific questions: Florida Property Appraisers’ Offices:Alachua County Property Appraiser – https://www.acpafl.org/Baker County Property Appraiser – https://www.bakerpa.com/Bay County Property Appraiser – https://baypa.net/Bradford County Property Appraiser – https://www.bradfordappraiser.com/Brevard County Property Appraiser – https://www.bcpao.us/Broward County Property Appraiser – https://bcpa.net/Calhoun County Property Appraiser – https://calhounpa.net/Charlotte County Property Appraiser – https://www.ccappraiser.com/Citrus County Property Appraiser – https://www.citruspa.org/Clay County Property Appraiser – https://ccpao.com/Collier County Property Appraiser – https://www.collierappraiser.com/Columbia County Property Appraiser – https://columbia.floridapa.com/DeSoto County Property Appraiser – https://www.desotopa.com/Dixie County Property Appraiser – https://www.qpublic.net/fl/dixie/Duval County Property Appraiser – https://www.coj.net/departments/property-appraiser.aspxEscambia County Property Appraiser – https://www.escpa.org/Flagler County Property Appraiser – https://flaglerpa.com/Franklin County Property Appraiser – https://franklincountypa.net/Gadsden County Property Appraiser – https://gadsdenpa.com/Gilchrist County Property Appraiser – https://www.qpublic.net/fl/gilchrist/Glades County Property Appraiser – https://qpublic.net/fl/glades/Gulf County Property Appraiser – https://gulfpa.com/Hamilton County Property Appraiser – https://hamiltonpa.com/Hardee County Property Appraiser – https://hardeepa.com/Hendry County Property Appraiser – https://hendryprop.com/Hernando County Property Appraiser – https://www.hernandopa-fl.us/PAWEBSITE/Default.aspxHighlands County Property Appraiser – https://www.hcpao.org/Hillsborough County Property Appraiser – https://www.hcpafl.org/Holmes County Property Appraiser – https://www.qpublic.net/fl/holmes/Indian River County Property Appraiser – https://www.ircpa.org/Jackson County Property Appraiser – https://www.qpublic.net/fl/jackson/Jefferson County Property Appraiser – https://jeffersonpa.net/Lafayette County Property Appraiser – https://www.lafayettepa.com/Lake County Property Appraiser – https://www.lakecopropappr.com/Lee County Property Appraiser – https://www.leepa.org/Leon County Property Appraiser – https://www.leonpa.gov/Levy County Property Appraiser – https://www.qpublic.net/fl/levy/Liberty County Property Appraiser – https://libertypa.org/Madison County Property Appraiser – https://madisonpa.com/Manatee County Property Appraiser – https://www.manateepao.gov/Marion County Property Appraiser – https://www.pa.marion.fl.us/Martin County Property Appraiser – https://www.pa.martin.fl.us/Miami-Dade County Property Appraiser – https://www.miamidade.gov/pa/Monroe County Property Appraiser – https://mcpafl.org/Nassau County Property Appraiser – https://www.nassauflpa.com/Okaloosa County Property Appraiser – https://okaloosapa.com/Okeechobee County Property Appraiser – https://www.okeechobeepa.com/Orange County Property Appraiser – https://ocpaweb.ocpafl.org/Osceola County Property Appraiser – https://www.property-appraiser.org/Palm Beach County Property Appraiser – https://www.pbcgov.org/papa/index.htmPasco County Property Appraiser – https://pascopa.com/Pinellas County Property Appraiser – https://www.pcpao.org/Polk County Property Appraiser – https://www.polkpa.org/Putnam County Property Appraiser – https://pa.putnam-fl.com/Santa Rosa County Property Appraiser – https://srcpa.gov/Sarasota County Property Appraiser – https://www.sc-pa.com/Seminole County Property Appraiser – https://www.scpafl.org/St. Johns County Property Appraiser – https://www.sjcpa.gov/St. Lucie County Property Appraiser – https://www.paslc.gov/Sumter County Property Appraiser – https://www.sumterpa.com/Suwannee County Property Appraiser – https://suwannee.floridapa.com/Taylor County Property Appraiser – https://qpublic.net/fl/taylor/Union County Property Appraiser – https://union.floridapa.com/Volusia County Property Appraiser – https://vcpa.vcgov.org/Wakulla County Property Appraiser – https://mywakullapa.com/Walton County Property Appraiser – https://waltonpa.com/Washington County Property Appraiser – https://www.qpublic.net/fl/washington/Florida Department of Revenue Property Tax Oversight https://floridarevenue.com/property/Pages/Home.aspx

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

For the implementation of the European Union Water Framework Directive (WFD), technological and scientific support are required. This paper presents a methodology to support a first step of the implementation of WFD, which is the delineation of groundwater bodies. The methodology consists of (1) the development of a complete and generally-accepted hydrogeological classification system for Flanders, named the HCOV code, (2) the development of a geographic information systems (GIS)-managed borehole database, and (3) the development of aquifer and aquitard models by means of a solid modeling approach. For each unit of the hydrogeological classification code for Flanders unit, GIS maps are generated for the three basic characteristics of hydrogeological layers: extent, base level and thickness, such that combined, the volume and extent of a hydrogeological layer is unambiguously defined. This GIS-based hydrogeological database has become a useful tool for groundwater management purposes and to provide the input for groundwater modeling.

NOTE: DO NOT DOWNLOAD THE IMAGERY BY USING THE MAP OR DOWNLOAD TOOLS ON THIS ARCGIS HUB ITEM PAGE. IT WILL RESULT IN A PIXELATED ORTHOIMAGE. INSTEAD, DOWNLOAD THE IMAGERY BY TILE OR BY COUNTY MOSAIC (2010 - current year).This service depicts true color imagery for the 21 counties representing the Northern Piedmont and Mountains region of North Carolina. This includes the following counties: Anson, Buncombe, Cabarrus, Cherokee, Clay, Cleveland, Gaston, Graham, Haywood, Henderson, Jackson, Lincoln, Macon, Mecklenburg, Montgomery, Polk, Rutherford, Stanly, Swain, Transylvania, and Union. The imagery has a pixel resolution of 6 inches and was flown in the beginning of 2019. The RMSE is 1.5 ft X and Y. Individual pixel values may have been altered during image processing. Therefore, this service should be used for general reference and viewing. Image analysis requiring examination of individual pixel values is discouraged. To view the latest imagery for any location in the state, customers should use the "Orthoimagery_Latest" image service (https://services.nconemap.gov/secure/rest/services/Imagery/Orthoimagery_Latest/ImageServer).To find specific dates the images were captured use the imagery dates app or download the data.Metadata:Summary metadata for orthoimagery mosaicsSummary metadata for orthoimagery tilesContractor-specific metadata for Cherokee, Clay, Graham, Macon, and Swain countiesContractor-specific metadata for Buncombe, Haywood, Henderson, Jackson, Transylvania countiesContractor-specific metadata for Cleveland, Gaston, Lincoln, Polk, and Rutherford countiesContractor-specific metadata for Anson, Cabarrus, Mecklenburg, Montgomery, Stanly, and Union counties

Peste des petits ruminants virus (PPRV), responsible for peste des petits ruminants (PPR), is widely circulating in Africa and Asia. The disease is a huge burden for the economy and development of the affected countries. In Eastern Africa, the disease is considered endemic. Because of the geographic proximity and existing trade between eastern African countries and the Comoros archipelago, the latter is at risk of introduction and spread, and the first PPR outbreaks occurred in the Union of the Comoros in 2012. The objective of this study was to map the areas suitable for PPR occurrence and spread in the Union of the Comoros and four eastern African countries, namely Ethiopia, Uganda, Kenya, and Tanzania. A Geographic Information System (GIS)-based Multicriteria Evaluation (MCE) was developed. Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0.907; 95% CI [0.820–0.994]), and a very good performance in the Union of the Comoros (AUC = 0.889, 95% CI: [0.694–1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030.

The intensification of agriculture, especially in recent decades, the ever-increasing density of settlements and the increasingly dense transport network have led to a fragmentation of a formerly small-structured landscape and networked habitats. Negative consequences are the genetic impoverishment of the populations, a reduction in the number of species and a reduced biodiversity. One tool to stop this development is the biotope network. In principle, it expects to network the same or similar biotope types in order to restore functional relationships and enable the migration of plants and animals. The European Union calls for a coherent ecological network of protected areas to be connected by appropriate landscape features in accordance with Article 10 of the Habitats Directive.

This thesis is intended to provide initial basic information for a biotope network Styria on a GIS basis. The basic GIS analysis refers to a representation of the most important barriers and composite structures in Styria, a subdivision of Natura 2000 sites by landscape units, which are roughly equivalent to the biogeographical regions, and the calculation of distances between the Natura 2000 sites. Furthermore, GIS analyses, which contain distance calculations, intersectional analyses and habitat analyses, are carried out for FFH protected goods and protective conductive species. The main anthropogenic barriers in Styria include major transport routes, such as motorways, expressways and railroads and high-voltage lines, as well as naturally large flowing waters and mountain ranges. The most important composite elements are the edges of large flowing waters and large contiguous forests. see you. The distance between the Natura 2000 sites of the Obersteiermark (North and Central Alps) and those in the foreland area is considerable. However, a biotope network should be based on biogeographical regions anyway. The main interconnections between the regions are the valleys and rivers in Styria. Based on the exemplary species, the GIS analyses and presentations make it possible to make statements about the isolation and resulting recommendations on further measures.

For a sustainable biotope network in Styria, in addition to the Natura 2000 areas, habitats outside the protected areas must be included in the planning. The aim is to achieve a nationwide biotope network planning supported by remote sensing.

The map is made available in GPKG format (the filename is "full_geo_lim_3035.gpkg" - QGIS and other Open Source GIS can deal with this format) Along with the map, we provide: 1) the original national geological maps of Germany, Italy, Slovenia, France, Switzerland and Austria, used for creating the map (folder "original_maps" in the "scripts" folder); 2) a script ("build_geo_lim.sh" in the "scripts" folder) that can be used to replicate the classification and the union of the original maps. The script was tested in a Ubuntu 16.04 environment where GRASS GIS 7.4 is installed. In order to run the script, the following command must be executed inside the "scripts" folder: grass74 -c datagrass/geo_lim/geo_lim/ --exec bash build_geo_lim.sh The "sql" folder contains the rules (exploited by the main "build_geo_lim.sh" script) for the classification of the geological formations into lithological units. Users can conveniently change those rules in order to change the final classification. For the elaboration of the GIS-based simplified geo-lithological map (Geo-LiM), we took advantage of the geological layers, in vector format, extracted from (i) the geological map of Italy at 1:500,000 scale (http://www.isprambiente.gov.it), (ii) the geological map of Switzerland at 1:500,000 scale (http://www.swisstopo.admin.ch), (iii) the geological map of Germany at 1:1,000,000 scale (Geologische Karte der Bundesrepublik Deutschland 1:1,000,000, BGR, Hannover.), (iv) the geological map of Austria at 1:500,000 scale (www.geologie.ac.at), (v) the geological map of France at 1:1,000,000 scale, and (vi) the geological map of Slovenia at 1:250,000 scale. These two last maps were obtained from the European Geological Data Infrastructure (EGDI - http://www.europe-geology.eu/metadata/). The six maps are released in ESRI shapefile, having different coordinate reference systems and different accuracy and information quality. The layers of France, Germany and Slovenia contained some topological errors (e.g. gaps between polygon borders, overlapping polygon borders, etc...) and were corrected removing duplicate boundaries and areas smaller than, respectively, 1 square meter, 600 square meters and 50 square meters (the longest boundary with adjacent area was removed). M. Donnini was supported by a grant of the Fondazione Assicurazioni Generali, and A. Zucchini was partially supported by the research projects of Paola Comodi, Francesco Frondini e Diego Perugini of the Department of Physics and Geology of the University of Perugia. We introduce a new geo-lithological map of Central Europe (Geo-LiM) elaborated adopting a lithological classification compliant to the methods more used in the litterature for estimating the consumption of atmospheric CO2 due by chemical weathering. Geo-LiM represents a novelty if compared with published global geo-lithological maps. The first novelty is due by the attention paid in discriminating metamorphic rocks that were classified according to the chemistry of protoliths. The second novelty is that the procedure used for the definition of the map is made available on the web to allow the replicability and reproducibility of the product. Donnini, M., Marchesini, I., & Zucchini, A. (2020). Geo-LiM: a new geo-lithological map for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy) as a tool for the estimation of atmospheric CO2 consumption. Journal of Maps, 16(2), 43–55. https://doi.org/10.1080/17445647.2019.1692082 Donnini, M., Marchesini, I., & Zucchini, A. (2020). A new Alpine geo-lithological map (Alpine-Geo-LiM) and global carbon cycle implications. GSA Bulletin. https://doi.org/10.1130/B35236.1 {"references": ["Donnini, M., Marchesini, I., & Zucchini, A. (2020). Geo-LiM: a new geo-lithological map for Central Europe (Germany, France, Switzerland, Austria, Slovenia, and Northern Italy) as a tool for the estimation of atmospheric CO2 consumption. Journal of Maps, 16(2), 43\u201355. https://doi.org/10.1080/17445647.2019.1692082", "Donnini, M., Marchesini, I., & Zucchini, A. (2020). A new Alpine geo-lithological map (Alpine-Geo-LiM) and global carbon cycle implications. GSA Bulletin. https://doi.org/10.1130/B35236.1"]}

Environmental Justice Block Groups 2021 was created from Connecticut block group boundary data located in the Census Bureau's 2019 TIGER/Line Shapefiles. The poverty data used to determine which block groups qualified as EJ communities (see CT State statute 22a-20a) was based on the Census Bureau's 2019 ACS 5-year estimate- Table C17002. This poverty data was joined with the block group boundaries in ArcMap. Block groups in which the percent of the population below 200% of the federal poverty level was greater than or equal to 30.0 were selected and the resulting selection was exported as a new shapefile. The block groups were then clipped so that only those block groups outside of distressed municipalities were displayed. Maintenance – This layer will be updated annually and will coincide with the annual distressed municipalities update (around August/September). The latest ACS 5-year estimate data should be used to update this layer.

Environmental Justice Distressed Municipalities 2021 was created from Connecticut town boundary data located in the Census Bureau's 2019 TIGER/Line Shapefiles (County Subdivisions). From this shapefile, "select by attribute" was used to select the distressed municipalities by town name (note: the list of 2021 distressed municipalities was provided by the CT Department of Economic and Community Development). The selection was then exported a new shapefile. The “Union” tool was used to unite the new shapefile with tribal lands (American Indian Area Geography) boundary data from the 2019 TIGER/Line files. In the resulting layer, the tribal lands were deleted so only the distressed municipalities remained. Maintenance – This layer will be updated annually when the DECD produces its new list of distressed municipalities (around August/September).

This map uses an archive of Version 1.0 of the CEJST data as a fully functional GIS layer. See an archive of the latest version of the CEJST tool using Version 2.0 of the data released in December 2024 here.This map assesses and identifies communities that are Housing Disadvantaged according to Justice40 Initiative criteria. "Communities are identified as disadvantaged if they are in census tracts that:Experienced historic underinvestment OR are at or above the 90th percentile for the housing cost OR lack of green space OR lack of indoor plumbing OR lead paintAND are at or above the 65th percentile for low income"Census tracts in the U.S. and its territories that meet the criteria are shaded in blue colors. Suitable for dashboards, apps, stories, and grant applications.Details of the assessment are provided in the popup for every census tract in the United States and its territories American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands. This map uses 2010 census tracts from Version 1.0 of the source data downloaded November 22, 2022.Use this map to help plan for grant applications, to perform spatial analysis, and to create informative dashboards and web applications.From the source:This data "highlights disadvantaged census tracts across all 50 states, the District of Columbia, and the U.S. territories. Communities are considered disadvantaged:If they are in census tracts that meet the thresholds for at least one of the tool’s categories of burden, orIf they are on land within the boundaries of Federally Recognized TribesCategories of BurdensThe tool uses datasets as indicators of burdens. The burdens are organized into categories. A community is highlighted as disadvantaged on the CEJST map if it is in a census tract that is (1) at or above the threshold for one or more environmental, climate, or other burdens, and (2) at or above the threshold for an associated socioeconomic burden.In addition, a census tract that is completely surrounded by disadvantaged communities and is at or above the 50% percentile for low income is also considered disadvantaged.Census tracts are small units of geography. Census tract boundaries for statistical areas are determined by the U.S. Census Bureau once every ten years. The tool utilizes the census tract boundaries from 2010. This was chosen because many of the data sources in the tool currently use the 2010 census boundaries."PurposeThe goal of the Justice40 Initiative is to provide 40 percent of the overall benefits of certain Federal investments in [eight] key areas to disadvantaged communities. These [eight] key areas are: climate change, clean energy and energy efficiency, clean transit, affordable and sustainable housing, training and workforce development, the remediation and reduction of legacy pollution, [health burdens] and the development of critical clean water infrastructure." Source: Climate and Economic Justice Screening tool"Sec. 219. Policy. To secure an equitable economic future, the United States must ensure that environmental and economic justice are key considerations in how we govern. That means investing and building a clean energy economy that creates well‑paying union jobs, turning disadvantaged communities — historically marginalized and overburdened — into healthy, thriving communities, and undertaking robust actions to mitigate climate change while preparing for the impacts of climate change across rural, urban, and Tribal areas. Agencies shall make achieving environmental justice part of their missions by developing programs, policies, and activities to address the disproportionately high and adverse human health, environmental, climate-related and other cumulative impacts on disadvantaged communities, as well as the accompanying economic challenges of such impacts. It is therefore the policy of my Administration to secure environmental justice and spur economic opportunity for disadvantaged communities that have been historically marginalized and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care." Source: Executive Order on Tackling the Climate Crisis at Home and AbroadUse of this Data"The pilot identifies 21 priority programs to immediately begin enhancing benefits for disadvantaged communities. These priority programs will provide a blueprint for other agencies to help inform their work to implement the Justice40 Initiative across government." Source: The Path to Achieving Justice 40

North Carolina Effective Flood zones: In 2000, the Federal Emergency Management Agency (FEMA) designated North Carolina a Cooperating Technical Partner State, formalizing an agreement between FEMA and the State to modernize flood maps. This partnership resulted in creation of the North Carolina Floodplain Mapping Program (NCFMP). As a CTS, the State assumed primary ownership and responsibility of the Flood Insurance Rate Maps (FIRMs) for all North Carolina communities as part of the National Flood Insurance Program (NFIP). This project includes conducting flood hazard analyses and producing updated, Digital Flood Insurance Rate Maps (DFIRMs). Floodplain management is a process that aims to achieve reduced losses due to flooding. It takes on many forms, but is realized through a series of federal, state, and local programs and regulations, in concert with industry practice, to identify flood risk, implement methods to protect man-made development from flooding, and protect the natural and beneficial functions of floodplains. FIRMs are the primary tool for state and local governments to mitigate areas of flooding. Individual county databases can be downloaded from https://fris.nc.gov Updated Jan 17th, 2025.