This paper discusses the role of various spatial techniques available for extracting the floodinundation map for river basins. Flood inundation mapping is a vital component for appropriate landuse planning in flood-prone areas. In this paper examples of some previous case studies are included to illustrate how spatial techniques are being used now days for preparing flood hazard mapping.

👨‍💻 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...

Spatial thinking skills are crucial for success in any of the STEM (science, technology, engineering, and mathematics) domains, and they are malleable. One approach to support the development of student spatial skills is through the use of innovative technologies, like the augmented reality (AR) sandbox, that can also effectively teach geoscience content in the process. In this study, we aimed to create a student-informed spatial topographic map activity designed to emphasize mental rotation, spatial orientation, and spatial visualization skills using the AR sandbox that incorporated elements such as drawing topographic profiles and recognizing stream flow direction. Furthermore, this study explored the spatial reasoning beliefs and challenges of undergraduate students at a large-enrollment Southeastern US university. Both quantitative and qualitative measures were employed to better understand student performance on and challenges with the topographic map and spatial tasks. Overall, the students found spatial visualization tasks in the activity to be the most challenging, and they were least confident in their spatial visualization skills. However, they believed that their spatial visualization skills were most improved after completing the topographic map activity, and those activities were reported to be the most effective at getting them to think in spatial terms. These results highlight that multi-step mental manipulations required to perform spatial visualization tasks are of great interest to instructors when developing topographic map activities using the AR sandbox. With more investigation, the AR sandbox has the potential to aid in the development of students’ spatial visualization skills while simultaneously teaching geological content. Supplemental data for this article is available online at https://doi.org/10.1080/10899995.2021.1969862

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance applications,

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

eXtension Foundation, the University of New Hampshire, and Virginia Tech have developed a mapping and data exploration tool to assist Cooperative Extension staff and administrators in making strategic planning and programming decisions. The tool, called the National Extension Web-mapping Tool (or NEWT), is the key in efforts to make spatial data available within cooperative extension system. NEWT requires no GIS experience to use. NEWT provides access for CES staff and administrators to relevant spatial data at a variety of scales (national, state, county) in useful formats (maps, tables, graphs), all without the need for any experience or technical skills in Geographic Information System (GIS) software. By providing consistent access to relevant spatial data throughout the country in a format useful to CES staff and administrators, NEWT represents a significant advancement for the use of spatial technology in CES. Users of the site will be able to discover the data layers which are of most interest to them by making simple, guided choices about topics related to their work. Once the relevant data layers have been chosen, a mapping interface will allow the exploration of spatial relationships and the creation and export of maps. Extension areas to filter searches include 4-H Youth & Family, Agriculture, Business, Community, Food & Health, and Natural Resources. Users will also be able to explore data by viewing data tables and graphs. This Beta release is open for public use and feedback. Resources in this dataset:Resource Title: Website Pointer to NEWT National Extension Web-mapping Tool Beta. File Name: Web Page, url: https://www.mapasyst.org/newt/ The site leads the user through the process of selecting the data in which they would be most interested, then provides a variety of ways for the user to explore the data (maps, graphs, tables).

Land Cover Mapping Analysis And Urban Growth Modelling Using Remote Sensing Techniques In Greater Cairo RegionEgypt
This dataset falls under the category Traffic Generating Parameters Land Cover.
It contains the following data: This study modelled the urban growth in the Greater Cairo Region (GCR), one of the fastest growing mega cities in the world, using remote sensing data and ancillary data. Three land use land cover (LULC) maps (1984, 2003 and 2014) were produced from satellite images by using Support Vector Machines (SVM). Then, land cover changes were detected by applying a high level mapping technique that combines binary maps (change/no-change) and post classification comparison technique. The spatial and temporal urban growth patterns were analyzed using selected statistical metrics developed in the FRAGSTATS software. Major transitions to urban were modelled to predict the future scenarios for year 2025 using Land Change Modeller (LCM) embedded in the IDRISI software. The model results, after validation, indicated that 14% of the vegetation and 4% of the desert in 2014 will be urbanized in 2025. The urban areas within a 5-km buffer around: the Great Pyramids, Islamic Cairo and Al-Baron Palace were calculated, highlighting an intense urbanization especially around the Pyramids; 28% in 2014 up to 40% in 2025. Knowing the current and estimated urbanization situation in GCR will help decision makers to adjust and develop new plans to achieve a sustainable development of urban areas and to protect the historical locations.
This dataset was scouted on 2022-02-03 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.researchgate.net/publication/282321895_Land_Cover_Mapping_Analysis_and_Urban_Growth_Modelling_Using_Remote_Sensing_Techniques_in_Greater_Cairo_Region-Egypt Please note: This link leads to an external resource. If you experience any issues with its availability, please try again later.

Abstract Precipitation is an important object of study and plays an important role in the dynamics of rainfall distribution in a region. This study investigated the spatial and temporal variation of precipitation in the São Francisco River Basin (SFRB). A historical series of data from 1989 to 2018 was analyzed, and a random function was decomposed into trend and residual components for analysis of precipitation. Interpolation techniques were used to analyze precipitation spatial behavior over time, using high-resolution precipitation maps. Our results showed that the exponential model prevailed in four periods. The findings also showed a high precipitation variability in the SFRB and enabled us to monitor precipitation behavior over the years, as well as in the different sub-regions in SFRB. Finally, important information was obtained, enabling, for instance, the identification of vulnerable areas suffering from lack of rainfall.

The dataset comprises mental maps of five cities in CEE/FSU produced by a broad spectrum of urban policymakers, understood here as any actors who influence the formulation of urban policies. These actors fall into three categories:Public officials (both elected and appointed), including civil servants and urban planners employed by municipal administrations at managerial and subordinate levels;Business representatives - primarily real-estate developers, property owners, and employees of IT companies whose operations depend on urban space, and companies active in the leisure and tourism sectors;Urban reviewers - individuals who critically evaluate urban issues, such as analysts, researchers, journalists, and activists.However, these roles may overlap when someone has experience in multiple fields.Respondents’ sketches were collected during individual semi-structured, in-depth interviews (IDI) conducted with the aforementioned policymakers in five cities: Leipzig (Germany), Warsaw (Poland), Kyiv and Lviv (Ukraine), and Tallinn (Estonia). The material was gathered during field-research trips carried out between 2021 and 2024. Ukrainian interviews occurred in the summer and autumn of 2021, shortly before Russia’s full-scale invasion. Although the study focuses on so-called post-socialist cities, the dataset can be used for broader investigations of contemporary urbanization processes.During the interviews, respondents were asked to identify positive, neutral, and negative elements, places, and problems relevant to their city using three colors - green for positives, black for neutrals, and red for negatives. They produced freehand sketches in any technique they preferred - stand-alone cartographic renderings, perspective drawings, mind, or word maps. To minimize interviewer influence, no base maps (printed or digital) were provided; respondents generated their associative maps entirely from memory.

This project evaluated ecosystem damage and recovery by developing a time series of habitat maps for the Rookery Bay National Estuarine Research Reserve. Habitat maps were created based on WorldView-2 and Landsat-8 satellite imagery from 2010-2018 using an automated technique and validated with a field campaign. Landsat images were mapped using the Support Vector Machine machine learning method...

The development of an operational sea ice mapping system. This metadata record refers to the development and testing of an prototype system, ICEMAPPER, to interpret NOAA AVHRR imagery on a semi-automatic basis, off the Southern Ocean near to the Antarctic coast. From the abstract of one of the referenced papers: This paper reports work towards the development of a semi-automated technique for creating sea-ice and cloud maps from Advanced Very High Resolution Radiometer (AVHRR) images of the Southern Ocean near to the Antarctic coast. The technique is implemented as a computer-based system which applies a number of classification rules to the five bands of an AVHRR image and classifies each pixel in the image as representing open water, low cloud, high cloud or one of several different sea ice concentration categories. The map produced by the system is then displayed and an experienced sea ice forecaster evaluates the result. If it is deemed satisfactory the map is saved on disk. If not, the expert can alter various parameters within the classification rules to produce a satisfactory map. Experience so far has shown that judicious, but reasonably minor, changes to the rule parameters can produce a satisfactory sea-ice map relatively quickly in most cases. The system is also capable of effectively distinguishing cloudy from clear pixels but it does not accurately distinguish high cloud from low cloud in some of the images. Current work is being undertaken to improve the cloud classification rules.

Dataset Summary:

The Monterey, San Benito and San Luis Obispo County Impervious Surfaces map is a 5-class fine-scale polygon vector representation of artificial impervious surfaces in the region. There are 1,253,729 features in the dataset. Non-impervious areas are not mapped and are not covered by polygons. The impervious map represents the state of the landscape in summer, 2022. This technical mapping work for this product was conducted by the impervious mapping team at the University of Vermont Spatial Analysis Lab and EarthDefine. Table 1 lists download locations for the dataset.Table 1. Monterey, San Benito, and San Luis Obispo Counties counties impervious surfaces data

Description

Link

File Geodatabase Feature Class

https://vegmap.press/central_coast_impervious_fgdb

Vector Tile Service

https://vegmap.press/central_coast_impervious_vt

Detailed Dataset Description: The impervious map was created using a combination of AI techniques and “expert systems” rulesets developed in Trimble eCognition. Initial impervious polygons for populated areas were produced using AI techniques by EarthDefine. These were refined and classified into impervious types by the UVM Spatial Analysis Lab using eCognition. Impervious surfaces in less populated areas were produced entirely by the UVM Spatial Analysis Lab in eCognition. ECognition rulesets combine automated image segmentation with-object based image classification techniques. In contrast with machine learning approaches, expert systems rulesets are developed heuristically based on the knowledge of experienced image analysts. Key data sets used in the expert systems rulesets for impervious mapping included: high resolution (0.6 meter) 4-band NAIP imagery (2022), the unified lidar point cloud, which is an amalgamation of the most recent/best available lidar data for the three-county area, and lidar derived rasters from the unified point cloud such as the canopy height model (CHM) and normalized digital surface model (nDSM).

After production in AI and eCognition, the preliminary impervious map product was manually edited by a team of UVM’s photo interpreters. Manual editing corrected errors where the automated methods produced incorrect results.

The impervious map has 5 classes representing different types of impervious features, which are described below:

·
Building – Structures including homes, commercial buildings, outbuildings, and other human-made structures such as water tanks and silage silos. Structures fully occluded by vegetation will not be mapped.

·
Paved Road – Roads that are paved and wide enough for a vehicle.

· Dirt/Gravel Road – Dirt or gravel roads wide enough for a vehicle. Non-ephemeral fire roads, ranch roads and long driveways. Polygons representing narrow unpaved (single track) trails are not included in this data product.

· Other Dirt/Gravel Surface – Dirt or gravel surfaces that are highly compacted and used by humans and equipment, such as parking lots, road pull-offs, some dirt or gravel paths, and highly compacted areas around commercial activities. This class DOES NOT include natural turf playing fields, very lightly used dirt roads, livestock areas, naturally occurring bare soil or rock, or bare areas around ponds.

· Other Paved Surface – Includes parking lots, sidewalks, paved walking paths, swimming pools, tennis courts.

Miscellaneous quality control and processing notes:·
Zoom level used during manual quality control was no finer than 1 to 500.·
Vector data was created with no overlapping polygons.

Data Limitations: This is not a planimetric data product and was created using semi-automated techniques. It provides a reasonable and useful depiction of impervious surfaces for planners and managers but does not have the accuracy or precision to support engineering applications. No formal accuracy assessment was conducted for this dataset. Users should apply caution when using the data for applications requiring high positional or classification accuracy. Appropriate uses of the data product include:· As an input to storm water models· For planners to assess % imperviousness in a parcel/watershed· To help identify areas of human infrastructure for fuels and fire management· As an input to fuel models that are used in fire behavior and fire spread models· For cartography and mapping· Generally for use at scales 1:1,000 and smaller

Inappropriate uses of this product include:· Measuring exact square footage of structures or impervious features for building projects· Using the impervious polygons as geographically precise information for transportation and public works engineering projects· Determining ownership or maintenance responsibility of a particular feature, such as a paved or dirt road· Identifying publicly accessible areas for recreation or other uses· Confirming the suitability of a surface for any use including driving, hiking, bicycling, etc.

Common errors in this dataset are inter-class confusion and errors of commission to impervious. These are discussed in more detail below:

·
Inter-Class Confusion: The accuracy of the map for impervious versus pervious is very high (although no quantitative assessment of accuracy was funded for this product). However, the accuracy for individual impervious classes will be much lower. For example, confusion exists between the ‘Other Paved’ and ‘Other Dirt/Gravel Surface,’ classes, even though these are both mapped correctly as impervious surfaces.

· Errors of Commission: The most widespread error in this map are areas mapped as impervious that are actual pervious surfaces of dried out herbaceous land cover. Some dried-out herbaceous cover may be mistakenly classified as impervious due to spectral similarity. Manual editing minimized but did not completely eliminate these errors.

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

Shapefile Description:

The shapefile of India with district-level details is a geospatial dataset that provides detailed geographic boundaries and attributes of districts across the country.

Key Features:

• Geographic Boundaries: Contains precise polygon boundaries outlining each district within India's states, allowing for detailed mapping and spatial analysis.
• District-Level Details: Includes attributes such as district names, state affiliations, and potentially other demographic or administrative information.
• Projection and Coordinate System: Typically includes information on the geographic projection and coordinate system used, ensuring accurate mapping and integration with other spatial data.
• Usage: Useful for spatial analysis, visualization, and geographic information system (GIS) applications related to administrative divisions, resource management, and planning.

Applications:

• Mapping and Visualization: Enables the creation of detailed maps showing district boundaries and distribution across India.
• Spatial Analysis: Facilitates analysis of data at the district level, including demographic studies, resource allocation, and policy planning.
• Integration with Other Data: Can be combined with various datasets (e.g., crop production, economic indicators) for comprehensive analysis and reporting.

These are products of the Soil and Landscape Grid of Australia Facility generated through disaggregation of the Western Australian soil mapping. There are 9 soil attribute products available from the Soil Facility: Available Water Holding Capacity - Volumetric (AWC); Bulk Density - Whole Earth (BDw); Bulk Density - Fine Earth (BDf); Clay (CLY); Course Fragments (CFG); Electrical Conductivity (ECD); pH Water (pHw); Sand (SND); Silt (SLT).

Each soil attribute product is a collection of 6 depth slices. Each depth raster has an upper and lower uncertainty limit raster associated with it. The depths provided are 0-5cm, 5-15cm, 15-30cm, 30-60cm, 60-100cm & 100-200cm, consistent with the Specifications of the GlobalSoilMap.

The DSMART tool (Odgers et al. 2014) tool was used in a downscaling process to translate legacy soil landscape mapping to 3” resolution (approx. 100m cell size) raster predictions of soil classes (Holmes et al. Submitted). The soil class maps were then used to produce corresponding soil property surfaces using the PROPR tool (Odgers et al. 2015; Odgers et al. Submitted). Legacy mapping was compiled for the state of WA from surveys ranging in map scale from 1:20,000 to 1:2,000,000 (Schoknecht et al., 2004). The polygons are attributed with the soils and proportions of soils within polygons however individual soils were not explicitly spatially defined. These new disaggregated map products aim to incorporate expert soil surveyor knowledge embodied in legacy polygon soil maps, while providing re-interpreted soil spatial information at a scale that is more suited to on-ground decision making.

Note: The DSMART-derived dissagregated legacy soil mapping products provide different spatial predictions of soil properties to the national TERN Soil Grid products derived by Cubist (data mining) and kriging based on site data by Viscarra Rossel et al. (Submitted). Where they overlap, the national prediction layers and DSMART products can be considered complementary predictions. They will offer varying spatial reliability (/ uncertainty) depending on the availability of representative site data (for national predictions) and the scale and expertise of legacy mapping. The national predictions and DSMART disaggregated layers have also been merged as a means to present the best available (lowest statistical uncertainty) data from both products (Clifford et al. In Prep).

Previous versions of this collection contained Depths layers. These have been removed as the units do not comply with Global Soil Map specifications. Lineage: The soil attribute maps are generated using novel spatial modelling and digital soil mapping techniques to disaggregate legacy soil mapping.

Legacy soil mapping: Polygon-based soil mapping for Western Australia’s agricultural zone was developed via WA’s Department of Agriculture and Food (Schoknecht et al., 2004). Seventy-three soil classes (termed ‘WA soil groups’ Schoknecht and Pathan, 2013) have been defined to capture the range of variation in soil profiles across this area. While legacy soil mapping does not explicitly map the distribution of these soil classes, estimates of their percentage composition and associated soil properties are available for each soil landscape map unit (polygon).

Disaggregation of soil classes: The DSMART algorithm (version 1, described in Odgers et al. 2014) was used to produce fine-resolution raster predictions for the probability of occurrence of each soil class. This uses random virtual sampling within each map unit (with sampling weighted by the expected proportions of each soil class) to build predictions for the distribution of soil classes based on relationships with environmental covariate layers (e.g. elevation, terrain attributes, climate, remote sensing vegetation indices, radiometrics). The algorithm was run 100 times then averaged to create probabilistic estimates for soil class spatial distributions.

Soil property predictions: The PROPR algorithm (Odgers et al. 2015) was used to generate soil property maps (and their associated uncertainty) using reference soil property data and the soil class probability maps create through the above DSMART disaggregation step.

Western Australia’s expert defined typical range of soil properties by soil class was used to provide reference soil properties to PROPR. These estimates were made separately for each physiographic zone across WA, and are based on available profile data and surveyor experience. Uncertainty bounds were determined by the minimum and maximum soil properties at the ‘qualified soil group’ level, and the property value of the most common soil in the map unit was used to define the typical soil property. This methodology was previously developed to meet the specifications of McKenzie et al. (2012) and provides expert soil surveyor estimates for map unit area composition and representative profile properties. Depth averaging was applied to the regional variant profile data to obtain property values at the specified GlobalSoilMap depth intervals. Then area-weighted soil property averages were calculated for each subgroup soil class. This process is documented further in Odgers et al. (Submitted).

[From The Landmap Project: Introduction, "http://www.landmap.ac.uk/background/intro.html"]

 A joint project to provide orthorectified satellite image mosaics of Landsat,
 SPOT and ERS radar data and a high resolution Digital Elevation Model for the
 whole of the UK. These data will be in a form which can easily be merged with
 other data, such as road networks, so that any user can quickly produce a
 precise map of their area of interest.

 Predominately aimed at the UK academic and educational sectors these data and
 software are held online at the Manchester University super computer facility
 where users can either process the data remotely or download it to their local
 network.

 Please follow the links to the left for more information about the project or
 how to obtain data or access to the radar processing system at MIMAS. Please
 also refer to the MIMAS spatial-side website,
 "http://www.mimas.ac.uk/spatial/", for related remote sensing materials.

CrimeMapTutorial is a step-by-step tutorial for learning crime mapping using ArcView GIS or MapInfo Professional GIS. It was designed to give users a thorough introduction to most of the knowledge and skills needed to produce daily maps and spatial data queries that uniformed officers and detectives find valuable for crime prevention and enforcement. The tutorials can be used either for self-learning or in a laboratory setting. The geographic information system (GIS) and police data were supplied by the Rochester, New York, Police Department. For each mapping software package, there are three PDF tutorial workbooks and one WinZip archive containing sample data and maps. Workbook 1 was designed for GIS users who want to learn how to use a crime-mapping GIS and how to generate maps and data queries. Workbook 2 was created to assist data preparers in processing police data for use in a GIS. This includes address-matching of police incidents to place them on pin maps and aggregating crime counts by areas (like car beats) to produce area or choropleth maps. Workbook 3 was designed for map makers who want to learn how to construct useful crime maps, given police data that have already been address-matched and preprocessed by data preparers. It is estimated that the three tutorials take approximately six hours to complete in total, including exercises.

This web map supports the US Caribbean Spatial Prioritization 2019 Results Web Application: https://noaa.maps.arcgis.com/home/item.html?id=537dfa0a18334b1c82e0f691b85c2848This map was developed by NOAA’s National Center for Coastal Ocean Science (NCCOS) to help respondents identify priorities for seafloor mapping and visual surveys in the US Caribbean. Identifying cross-organizational priorities will enable participating agencies to more effectively coordinate assets, and efficiently guide future mapping, research, and exploration in Puerto Rico and US Virgin Islands. The results from this effort will be analyzed using several statistical techniques to identify significant relationships between priorities, issues, and ranking criteria. This project is funded by NCCOS and NOAA’s Coral Reef Conservation Program.This map also contains the normalized coin totals from the West Coast Spatial Prioritization participants for Justifications, Data Products, and other analyses of the 2019 prioritization exercise.Please visit the project page for more information

Spatial Mapping of HITECH Grants

Visualizing Health and Community Grant Locations in the U.S

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides crucial geographic data related to two of the U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. As part of the American Recovery and Reinvestment Act (ARRA), these grants were made available to provide citizens with access to health IT infrastructure investments throughout diverse communities across the United States. This crosswalk is an essential resource for anyone looking to link regional, state, county and zip code level program financials with performance metrics for visualization or comparison. With detailed information about region counties, codes, states, FIPS codes and ZIP codes associated with local HITECH grantees, this data presentation helps shed light on a financially impactful initiative from our federal government that can drastically improve healthcare delivery in thousands of cities nationwide!

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How to use the dataset

This dataset provides geographic data for the service areas of two of the HITECH programs, the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This can be used to map and visualize data related to those programs. Here is a guide on how to use this dataset:

• Get familiar with key columns: Familiarize yourself with the columns included in this dataset, including column names and descriptions for each column such as region, region_code, county_name, state_fips, county_fips and zip.
• Review data formats: If there are any discrepancies between your current format of data presented in this dataset versus what you may have currently in your system or within other sources of information - make sure to review those discrepancies prior exploring more from here onwards.
• Understand regional coverage: Refine the analysis by filtering out different grantee located based on specific regions or states - use necessary filters such as Region code or County FIPs code that will give you an easier view on which region/county grantee has been provided funding through these HHS programs as part of Hitech Act program distribution.
• Map & Visualize grantees: We can visualise geographically where are REC-Program & Beacon Communities Grants distributed across US by making a heatmap while taking desired geolocation coordinates like zip codes; query all available details under columns we need like zip codes along their respective countyp location & state value so that grants can be highlighted after it renders practical Map visuals for us giving an ease if further status / details required about entities who had taken these grants within certain area / regions!

Research Ideas

• Creating an interactive map to visualize grant program performance by region and county.
• Using the data to create a color-coded scatterplot graphic to show active grant program sites in the US.
• Generating reports on HITECH Grantee performance over time, grouped by geographic area or region

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: healthit-dashboard-areatype-crosswalk-csv-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | region | The region of the grantee. (String) | | region_code | The code for the region of the grantee. (String) | | county_name | The name of county where the grantee is located. (String) | | state_fips | The Federal Information Processing Standard (FIPS) code for knowledge of which state it is located in. (String) | | county_fips | The Federa...

A paper copy of the Geologic Map of the Catalina Core Complex and San Pedro Trough (Dickinson, 1992) was scanned and digitized by U.S. Geological Survey staff and contractors at the Southwest Field Office (Tucson, AZ) in 2000-2001 for input into an ArcInfo geographic information system (GIS). The resulting geologic map database (in ArcInfo format) can be queried in many ways to produce a variety of geologic maps. Digital base map data files. (topography, roadways, towns, and hydrography) are not included: they may be obtained from a variety of commercial and government sources. Geologic map graphics and plot files that are provided in the Open-File Report are representations of the digital database and are not designed to be cartographic products.