👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

This code creates a geographic map and a corresponding population cartogram side by side. They have the same colour coding to facilitate comparison. Users can modify this code to map their own data.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

The Human Geography Map (World Edition) web map provides a detailed vector basemap with a monochromatic style and content adjusted to support Human Geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Base, a simple basemap consisting of land areas in a very light gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in Introducing a Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

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

This is a subset of World Biomass Image Layer to focus on Central Asia and Caucasus Region. Use this web map to visualize and understand the Biomass for that region. Use image layer for your analysis. Plants play a central role in the carbon cycle by absorbing carbon dioxide from the atmosphere and incorporating it in the structure of the plant. Globally living plants contain 500 billion metric tons of carbon, more than 60 times the amount of carbon released to the atmosphere by humans each year. Understanding the distribution of the carbon stored in living plants, known as biomass, is key to estimating the effects of land use change on the climate.Dataset SummaryThis layer provides access to a 1-km cell-sized raster with data on the density of carbon stored in living plants in metric tons per hectare for the year 2000. It was published by the Oak Ridge National Laboratory Carbon Dioxide Information Analysis Center in 2008.The authors of these data request that they be cited as:Ruesch, Aaron, and Holly K. Gibbs. 2008. New IPCC Tier-1 Global Biomass Carbon Map For the Year 2000. Available online from the Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): Land and Geographic Unit Area Grids measure land areas in square kilometers and the mean Unit size (population-weighted) in square kilometers. The land area grid permits the summation of areas (net of permanent ice and water) at the same resolution as the population density, count, and urban-rural grids. The mean Unit size grids provide a quantitative surface that indicates the size of the input Unit(s) from which population count and density grids are derived. Additional global grids are created from the 30 arc-second grid at 1/4, 1/2, and 1 degree resolutions. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).

Gene Expression Omnibus (GEO) Dataset: GSE68086

This dataset, available on the Gene Expression Omnibus (GEO) platform, provides valuable insights into cancer diagnostics through the analysis of tumor-educated platelets (TEPs). It highlights the potential of liquid biopsies for non-invasive cancer detection across multiple cancer types.

Dataset Overview:

• Title: RNA-seq of tumor-educated platelets enables blood-based pan-cancer, multiclass, and molecular pathway cancer diagnostics.
• Organism: Homo sapiens
• Experiment Type: Expression profiling by high-throughput sequencing
• Sample Size: 283 blood platelet samples
  • 228 tumor-educated platelet (TEP) samples from patients with six different malignant tumors.
  • 55 samples from healthy individuals.

Cancer Types Included: - Non-small cell lung cancer - Colorectal cancer - Pancreatic cancer - Glioblastoma - Breast cancer - Hepatobiliary carcinomas

Methodology:

• Sample Collection: Blood platelets were isolated from whole blood using EDTA anti-coagulant.
• RNA Extraction: Total RNA was extracted from platelet pellets using the mirVana RNA isolation kit.
• Sequencing: cDNA synthesis and amplification were performed using the SMARTer Ultra Low RNA Kit, followed by Covaris shearing and Illumina HiSeq 2500 sequencing.
• Quality Control: Performed using Bioanalyzer 2100 with RNA 6000 Picochip, DNA 7500, and DNA High Sensitivity chips.

Data Processing:

• Quality control using Trimmomatic
• Mapping to the hg19 reference genome using STAR (version 2.3.0)
• Intron-spanning reads selected using Picard-tools (version 1.115)
• Read summarization using HTseq (version 0.6.1)

Data Structure:

• Samples: 285 columns (including controls)
• Features: 57,736 Ensembl gene IDs (rows)
• Data Type: Intron-spanning read counts

Files Included:

1. GSE68086_TEP_data_matrix.txt.gz (3.6 MB): Original gzipped text file containing intron-spanning RNA-seq read counts.
2. GSE68086_TEP_data_matrix.csv: Converted CSV file of the original data.
3. GSE68086_series_matrix.txt: Series matrix file containing detailed sample information.
4. GSE68086_series_matrix.csv: Converted CSV version of the series matrix file.

Potential Applications:

• Non-invasive cancer diagnostics: Exploring liquid biopsies for cancer detection.
• Identification of cancer-specific biomarkers.
• Study of cancer-induced changes in platelet RNA profiles.
• Comparative analysis across different cancer types.

Machine Learning Models for:

• Binary classification: Healthy vs. cancer patients.
• Multiclass classification: Distinguishing between different cancer types.
• Molecular pathway analysis for identifying cancer-specific pathways.

Importance:

This dataset offers significant potential for advancing cancer diagnostics by leveraging tumor-educated platelets as biomarkers for early detection and classification of various cancer types. It represents a promising approach to non-invasive, blood-based cancer screening using gene expression profiles.

Data Access and Analysis:

• GEO Accession: GSE68086
• Online Analysis: Available through GEO2R
• R Package: Data can be accessed and analyzed using the GEOquery package.

Citation: Best MG, Sol N, Kooi I, Tannous J, et al. RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics. Cancer Cell, 2015 Nov 9;28(5):666-676. PMID: 26525104

The research project 3D-MAPP develops a web-based methodology to obtain digital geodata via the combination of data analysis by human and machine. Through a quick and easy-to-use 3D Web visualization users are able – in a few seconds – to solve 3D micro mapping tasks, which can hardly or even not be solved by automatic algorithms.

Author: ANN WURST, NGS TEACHER CONSULTANTGrade/Audience: grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: activitySubject topic(s): cartography, maps, regional geographyRegion: worldStandards: TEXAS TEKS (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions; (B) create a product on a contemporary government issue or topic using critical methods of inquiry; (D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference; Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for world geo, AP Human Geo and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of cartography/map making , can be applied in every unit where map skills are needed. Helps further critical thinking skills.

The "Map Imager Layer - Administrative Boundaries" is a Map Image Layer of Administrative Boundaries. It has been designed specifically for use in ArcGIS Online (and will not directly work in ArcMap or ArcPro). This data has been modified from the original source data to serve a specific business purpose. This data is for cartographic purposes only.The Administrative Boundaries Data Group contains the following layers: Populated Places (USGS)US Census Urbanized Areas and Urban Clusters (USCB)US Census Minor Civil Divisions (USCB)PLSS Townships (MnDNR, MnGeo)Counties (USCB)American Indian, Alaska Native, Native Hawaiian (AIANNH) Areas (USCB)States (USCB)Countries (MPCA)These datasets have not been optimized for fast display (but rather they maintain their original shape/precision), therefore it is recommend that filtering is used to show only the features of interest. For more information about using filters please see "Work with map layers: Apply Filters": https://doc.arcgis.com/en/arcgis-online/create-maps/apply-filters.htmFor additional information about the Administrative Boundary Dataset please see:United States Census Bureau TIGER/Line Shapefiles and TIGER/Line Files Technical Documentation: https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlUnited States Census Bureau Census Mapping Files: https://www.census.gov/geographies/mapping-files.htmlUnited States Census Bureau TIGER/Line Shapefiles: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html and https://www.census.gov/cgi-bin/geo/shapefiles/index.php

To understand the relationship between place and politics, we must measure both political attitudes and the ways in which place is represented in the minds of individuals. In this paper, we assess a new measure of mental-representation of geography, in which survey respondents draw their own local communities on maps and describe them. This mapping measure has been used in Canada, the UK, Denmark, and the U.S. so far. We use a panel study in Canada to present evidence that these maps are both valid and reliable measures of a personally relevant geographic area, laying the measurement groundwork for the growing number of studies using this technology. We hope to set efforts to measure ‘place’ for the study of context and politics on firmer footing. Our validity assessments show that individuals are thinking about people and places with which they have regular contact when asked to draw their communities. Our reliability assessments show that people can draw more or less the same map twice, even when the exercise is repeated months later. Finally, we provide evidence that the concept of community is a tangible consideration in the minds of ordinary citizens and is not simply a normative aspiration or motivation.

This site provides free access to Iowa geographic map data, including aerial photography, orthophotos, elevation maps, and historical maps. The data is available through an on-line map viewer and through Web Map Service (WMS) connections for GIS. The site was developed by the Iowa State University Geographic Information Systems Support and Research Facility in cooperation with the Iowa Department of Natural Resources, the USDA Natural Resources Conservation Service, and the Massachusetts Institute of Technology. This site was first launched in March 1999.

The Human Geography Dark Map (World Edition) web map provides a detailed world basemap with a dark monochromatic style and content adjusted to support human geography information. Where possible, the map content has been adjusted so that it observes WCAG contrast criteria.This basemap, included in the ArcGIS Living Atlas of the World, uses 3 vector tile layers:Human Geography Dark Label, a label reference layer including cities and communities, countries, administrative units, and at larger scales street names.Human Geography Dark Detail, a detail reference layer including administrative boundaries, roads and highways, and larger bodies of water. This layer is designed to be used with a high degree of transparency so that the detail does not compete with your information. It is set at approximately 50% in this web map, but can be adjusted.Human Geography Dark Base, a simple basemap consisting of land areas in a very dark gray only.The vector tile layers in this web map are built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Learn more about this basemap from the cartographic designer in A Dark Version of the Human Geography Basemap.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layers referenced in this map.

The Geometrically Valuable Areas Signaling Map is a map showing a total overview of larger and smaller geologically interesting areas and elements in Zeeland. These areas are interesting because of landscape shape/history, soil type, current formation processes or special geology. The Earthly Valuable Areas Signaling Map forms the basis of provincial selection on the Earthly Valuable Area Map. However, the Signalering Map also contains areas that are not included in the provincial selection of geographically valuable areas but have a clear geographical and landscape significance.

A test resource to check the python api. Visit https://dataone.org/datasets/sha256%3Ab2476b888788447addba5a3a94d8bbdcf608f2c62f3d6110549dcbdcec4da6fb for complete metadata about this dataset.

These data were compiled for the use of training natural feature machine learning (GeoAI) detection and delineation. The natural feature classes include the Geographic Names Information System (GNIS) feature types Basins, Bays, Bends, Craters, Gaps, Guts, Islands, Lakes, Ridges and Valleys, and are an areal representation of those GNIS point features. Features were produced using heads-up digitizing from 2018 to 2019 by Dr. Sam Arundel's team at the U.S. Geological Survey, Center of Excellence for Geospatial Information Science, Rolla, Missouri, USA, and Dr. Wenwen Li's team in the School of Geographical Sciences at Arizona State University, Tempe, Arizona, USA.

Reason for SelectionThe Southeast United States is a global biodiversity hotspot that supports many rare and endemic reptile and amphibian species (Barrett et al. 2014, EPA 2014). These species are experiencing dramatic population declines driven by habitat loss, pollution, invasive species, and disease (Sutherland and deMaynadier 2012, EPA 2014, CI et al. 2004). Amphibians provide an early signal of environmental change because they rely on both terrestrial and aquatic habitats, are sensitive to pollutants, and are often narrowly adapted to specific geographic areas and climatic conditions. As a result, they serve as effective indicators of ecosystem health (CI et al. 2004, EPA 2014). Their association with particular microhabitats and microclimates makes amphibians vulnerable to climate change, and Southeast amphibians are predicted to lose significant amounts of climatically suitable habitat in the future (Barrett et al. 2014). PARCAs also represent the condition and arrangement of embedded isolated wetlands. Many amphibians breed in temporary (i.e., ephemeral) wetlands surrounded by upland habitat, which are not well-captured by existing indicators in the Blueprint (Erwin et al. 2016).Input DataSoutheast Blueprint 2024 extent2023 U.S. Census TIGER/Line state boundaries, accessed 4-5-2024: download the data Southeast Priority Amphibian and Reptile Conservation Areas (PARCAs) PARCAs for all Southeast states except for Mississippi, Virginia, and Kentucky, shared by José Garrido with the Amphibian and Reptile Conservancy (ARC) on 3-5-2024PARCAs for Mississippi, shared by Luis Tirado with ARC on 4-26-2024 (these PARCAs were identified more recently and were not yet captured in ARC’s Southeast PARCAs dataset)South Atlantic PARCAs: Neuse Tar River PARCA (this PARCA was identified through a project funded by the South Atlantic Landscape Conservation Cooperative and is not yet captured in ARC’s Southeast PARCAs dataset; we added this PARCA after consultation with ARC staff) To view a map depicting some of the PARCAs provided, scroll to the bottom of the work page of the ARC website under the heading “PARCAs Nationwide”; to access the data, email info@ARCProtects.org. PARCA is a nonregulatory designation established to raise public awareness and spark voluntary action by landowners and conservation partners to benefit amphibians and/or reptiles. Areas are nominated using scientific criteria and expert review, drawing on the concepts of species rarity, richness, regional responsibility, and landscape integrity. Modeled in part after the Important Bird Areas program developed by BirdLife International, PARCAs are intended to be nationally coordinated but locally implemented at state or regional scales. Importantly, PARCAs are not designed to compete with existing landscape biodiversity initiatives, but to complement them, providing an additional spatially explicit layer for conservation consideration.
PARCAs are intended to be established in areas: capable of supporting viable amphibian and reptile populations, occupied by rare, imperiled, or at-risk species, and rich in species diversity or endemism. For example, species used in identifying the PARCAs in the Southeast include: alligator snapping turtle, Barbour’s map turtle, one-toed amphiuma, Savannah slimy salamander, Mabee’s salamander, dwarf waterdog, Neuse river waterdog, chicken turtle, spotted turtle, tiger salamander, rainbow snake, lesser siren, gopher frog, Eastern diamondback rattlesnake, Southern hognose snake, pine snake, flatwoods salamander, gopher tortoise, striped newt, pine barrens tree frog, indigo snake, and others. There are four major implementation steps: Regional PARC task teams or state experts can use the criteria and modify them when appropriate to designate potential PARCAs in their area of interest. Following the identification of all potential PARCAs, the group then reduces these to a final set of exceptional sites that best represent the area of interest. Experts and stakeholders in the area of interest collaborate to produce a map that identifies these peer-reviewed PARCAs. Final PARCAs are shared with the community to encourage the implementation of voluntary habitat management and conservation efforts. PARCA boundaries can be updated as needed. Mapping Steps Merge the three PARCA polygon datasets and convert from vector to a 30 m pixel raster using the ArcPy Feature to Raster function. Give all PARCAs a value of 1.Add zero values to represent the extent of the source data and to make it perform better in online tools. Convert to raster the TIGER/Line state boundaries for all SEAFWA states except for Virginia and Kentucky and assign them a value of 0. We excluded Virginia and Kentucky because PARCAs have not yet been identified for these states. Use the Cell Statistics “MAX” function to combine the two above rasters.As a final step, clip to the spatial extent of Southeast Blueprint 2024. Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code.Final indicator valuesIndicator values are assigned as follows:1 = Priority Amphibian and Reptile Conservation Area (PARCA) 0 = Not a PARCA (excluding Kentucky and Virginia)Known IssuesThe mapping of this indicator is relatively coarse and doesn’t always capture differences in pixel-level quality in the outer edge of PARCAs. For example, some PARCAs include developed areas.This indicator is binary and doesn’t capture the full continuum of value across the Southeast.The methods of combining expert knowledge and data in this indicator may have caused some poorly known and/or under-surveyed areas to be scored too low.This indicator underprioritizes important reptile and amphibian habitat in Kentucky and Virginia because PARCAs have not yet been identified for these areas. ARC is working to expand PARCAs to more states in the future.Because of the state-by-state PARCA development and review process, sometimes PARCA boundaries stop at the state line, though suitable habitat for reptiles and amphibians does not always follow jurisdictional boundaries.This indicator excludes “protected” PARCAs maintained by ARC that are too small and spatially explicit to share publicly due to concerns about poaching. As a result, it underprioritizes some important reptile and amphibian habitat. However, these areas are, with a few exceptions in northwest Arkansas and Tennessee, generally well-represented in the Blueprint due to their value for other indicators.This indicator contains small gaps 1-2 pixels wide between some adjoining PARCAs that likely should be continuous, often on either side of a state line. These are represented in the source data as separate polygons with tiny gaps between them, and these translate into gaps in the resulting indicator raster. This results from the PARCA digitizing process and does not reflect meaningful differences in priority.Disclaimer: Comparing with Older Indicator VersionsThere are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).Literature CitedAmphibian and Reptile Conservancy. Priority Amphibian and Reptile Conservation Areas (PARCAs). Revised February 7, 2024. Apodaca, Joseph. 2013. Determining Priority Amphibian and Reptile Conservation Areas (PARCAs) in the South Atlantic landscape, and assessing their efficacy for cross-taxa conservation: Geographic Dataset. [https://www.sciencebase.gov/catalog/item/59e105a1e4b05fe04cd000df]. Barrett, Kyle, Nathan P. Nibbelink, John C. Maerz; Identifying Priority Species and Conservation Opportunities Under Future Climate Scenarios: Amphibians in a Biodiversity Hotspot. Journal of Fish and Wildlife Management 1 December 2014; 5 (2): 282–297. [https://doi.org/10.3996/022014-JFWM-015]. Conservation International, International Union for the Conservation of Nature, NatureServe. 2004. Global Amphibian Assessment Factsheet. [https://www.natureserve.org/sites/default/files/amphibian_fact_sheet.pdf]. Environmental Protection Agency. 2014. Mean Amphibian Species Richness: Southeast. EnviroAtlas Factsheet. [https://enviroatlas.epa.gov/enviroatlas/DataFactSheets/pdf/ESN/MeanAmphibianSpeciesRichness.pdf]. Erwin, K. J., Chandler, H. C., Palis, J. G., Gorman, T. A., & Haas, C. A. (2016). Herpetofaunal Communities in Ephemeral Wetlands Embedded within Longleaf Pine Flatwoods of the Gulf Coastal Plain. Southeastern Naturalist, 15(3), 431–447. [https://www.jstor.org/stable/26454722]. Sutherland and deMaynadier. 2012. Model Criteria and Implementation Guidance for a Priority Amphibian and Reptile Conservation Area (PARCA) System in the USA. Partners in Amphibian and Reptile Conservation, Technical Publication PARCA-1. 28 pp. [https://parcplace.org/wp-content/uploads/2017/08/PARCA_System_Criteria_and_Implementation_Guidance_FINAL.pdf]. U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch. TIGER/Line Shapefile, 2023, U.S. Current State and Equivalent National. 2023. [https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html].