In order to improve the capacity of storage, exploration and processing of sensor data, a spatial DBMS was used and the Aquopts system was implemented.

In field surveys using different sensors on the aquatic environment, the existence of spatial attributes in the dataset is common, motivating the adoption of PostgreSQL and its spatial extension PostGIS. To enable the insertion of new data sets as well as new devices and sensing equipment, the database was modeled to support updates and provide structures for storing all the data collected in the field campaigns in conjunction with other possible future data sources. The database model provides resources to manage spatial and temporal data and allows flexibility to select and filter the dataset.

The data model ensures the storage integrity of the information related to the samplings performed during the field survey in an architecture that benefits the organization and management of the data. However, in addition to the storage specified on the data model, there are several procedures that need to be applied to the data to prepare it for analysis. Some validations are important to identify spurious data that may represent important sources of information about data quality. Other corrections are essential to tweak the data and eliminate undesirable effects. Some equations can be used to produce other factors that can be obtained from the combination of attributes. In general, the processing steps comprise a cycle of important operations that are directly related to the characteristics of the data set. Considering the data of the sensors stored in the database, an interactive prototype system, named Aquopts, was developed to perform the necessary standardization and basic corrections and produce useful data for analysis, according to the correction methods known in the literature.

The system provides resources for the analyst to automate the process of reading, inserting, integrating, interpolating, correcting, and other calculations that are always repeated after exporting field campaign data and producing new data sets. All operations and processing required for data integration and correction have been implemented from the PHP and Python language and are available from a Web interface, which can be accessed from any computer connected to the internet. The data access cab be access online (http://sertie.fct.unesp.br/aquopts), but the resources are restricted by registration and permissions for each user. After their identification, the system evaluates the access permissions and makes available the options of insertion of new datasets.

The source-code of the entire Aquopts system are available at: https://github.com/carmoafc/aquopts

The system and additional results were described on the official paper (under review)

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Geographic Information System Analytics Market Size 2024-2028

The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.

The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.
Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.

What will be the Size of the GIS Analytics Market during the forecast period?

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The geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.
GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.

How is this Geographic Information System Analytics Industry segmented?

The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

End-user

  Retail and Real Estate
  Government
  Utilities
  Telecom
  Manufacturing and Automotive
  Agriculture
  Construction
  Mining
  Transportation
  Healthcare
  Defense and Intelligence
  Energy
  Education and Research
  BFSI


Components

  Software
  Services


Deployment Modes

  On-Premises
  Cloud-Based


Applications

  Urban and Regional Planning
  Disaster Management
  Environmental Monitoring Asset Management
  Surveying and Mapping
  Location-Based Services
  Geospatial Business Intelligence
  Natural Resource Management


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    South Korea


  Middle East and Africa

    UAE


  South America

    Brazil


  Rest of World

By End-user Insights

The retail and real estate segment is estimated to witness significant growth during the forecast period.

The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.

The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, gover

Snapshot img

North America Geographic Information System Market Size 2025-2029

The geographic information system market size in North America is forecast to increase by USD 11.4 billion at a CAGR of 23.7% between 2024 and 2029.

The market is experiencing significant growth due to the increasing adoption of advanced technologies such as artificial intelligence, satellite imagery, and sensors in various industries. In fleet management, GIS software is being used to optimize routes and improve operational efficiency. In the context of smart cities, GIS solutions are being utilized for content delivery, public safety, and building information modeling. The demand for miniaturization of technologies is also driving the market, allowing for the integration of GIS into smaller devices and applications. However, data security concerns remain a challenge, as the collection and storage of sensitive information requires robust security measures. The insurance industry is also leveraging GIS for telematics and risk assessment, while the construction sector uses GIS for server-based project management and planning. Overall, the GIS market is poised for continued growth as these trends and applications continue to evolve.

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The Geographic Information System (GIS) market encompasses a range of technologies and applications that enable the collection, management, analysis, and visualization of spatial data. Key industries driving market growth include transportation, infrastructure planning, urban planning, and environmental monitoring. Remote sensing technologies, such as satellite imaging and aerial photography, play a significant role in data collection. Artificial intelligence and the Internet of Things (IoT) are increasingly integrated into GIS solutions for real-time location data processing and operational efficiency.
Applications span various sectors, including agriculture, natural resources, construction, and smart cities. GIS is essential for infrastructure analysis, disaster management, and land management. Geospatial technology enables spatial data integration, providing valuable insights for decision-making and optimization. Market size is substantial and growing, fueled by increasing demand for efficient urban planning, improved infrastructure, and environmental sustainability. Geospatial startups continue to emerge, innovating in areas such as telematics, natural disasters, and smart city development.

How is this market segmented and which is the largest segment?

The market 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.

Component

  Software
  Data
  Services


Deployment

  On-premise
  Cloud


Geography

  North America

    Canada
    Mexico
    US

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The Geographic Information System (GIS) market encompasses desktop, mobile, cloud, and server software for managing and analyzing spatial data. In North America, industry-specific GIS software dominates, with some commercial entities providing open-source alternatives for limited functions like routing and geocoding. Despite this, counterfeit products pose a threat, making open-source software a viable option for smaller applications. Market trends indicate a shift towards cloud-based GIS solutions for enhanced operational efficiency and real-time location data. Spatial data applications span various sectors, including transportation infrastructure planning, urban planning, natural resources management, environmental monitoring, agriculture, and disaster management. Technological innovations, such as artificial intelligence, the Internet of Things (IoT), and satellite imagery, are revolutionizing GIS solutions.

Cloud-based GIS solutions, IoT integration, and augmented reality are emerging trends. Geospatial technology is essential for smart city projects, climate monitoring, intelligent transportation systems, and land management. Industry statistics indicate steady growth, with key players focusing on product innovation, infrastructure optimization, and geospatial utility solutions.

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Market Dynamics

Our North America Geographic Information System Market researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise in the adoption of the North America Geographic Information System Market?

Rising applications of geographic

Geographic Information System (GIS) Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to grow from USD 9.1 billion in 2023 to USD 18.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 8.5% over the forecast period. This growth is driven by the increasing application of GIS software across various sectors such as agriculture, construction, transportation, and utilities, along with the rising demand for location-based services and advanced mapping solutions.

One of the primary growth factors for the GIS software market is the widespread adoption of spatial data by various industries to enhance operational efficiency. In agriculture, for instance, GIS software plays a crucial role in precision farming by aiding in crop monitoring, soil analysis, and resource management, thereby optimizing yield and reducing costs. In the construction sector, GIS software is utilized for site selection, design and planning, and infrastructure management, making project execution more efficient and cost-effective.

Additionally, the integration of GIS with emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) is significantly enhancing the capabilities of GIS software. AI-driven data analytics and IoT-enabled sensors provide real-time data, which, when combined with spatial data, results in more accurate and actionable insights. This integration is particularly beneficial in fields like smart city planning, disaster management, and environmental monitoring, further propelling the market growth.

Another significant factor contributing to the market expansion is the increasing government initiatives and investments aimed at improving geospatial infrastructure. Governments worldwide are recognizing the importance of GIS in policy-making, urban planning, and public safety, leading to substantial investments in GIS technologies. For example, the U.S. governmentÂ’s Geospatial Data Act emphasizes the development of a cohesive national geospatial policy, which in turn is expected to create more opportunities for GIS software providers.

Geographic Information System Analytics is becoming increasingly pivotal in transforming raw geospatial data into actionable insights. By employing sophisticated analytical tools, GIS Analytics allows organizations to visualize complex spatial relationships and patterns, enhancing decision-making processes across various sectors. For instance, in urban planning, GIS Analytics can identify optimal locations for new infrastructure projects by analyzing population density, traffic patterns, and environmental constraints. Similarly, in the utility sector, it aids in asset management by predicting maintenance needs and optimizing resource allocation. The ability to integrate GIS Analytics with other data sources, such as demographic and economic data, further amplifies its utility, making it an indispensable tool for strategic planning and operational efficiency.

Regionally, North America holds the largest share of the GIS software market, driven by technological advancements and high adoption rates across various sectors. Europe follows closely, with significant growth attributed to the increasing use of GIS in environmental monitoring and urban planning. The Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, infrastructure development, and government initiatives in countries like China and India.

Component Analysis

The GIS software market is segmented into software and services, each playing a vital role in meeting the diverse needs of end-users. The software segment encompasses various types of GIS software, including desktop GIS, web GIS, and mobile GIS. Desktop GIS remains the most widely used, offering comprehensive tools for spatial analysis, data management, and visualization. Web GIS, on the other hand, is gaining traction due to its accessibility and ease of use, allowing users to access GIS capabilities through a web browser without the need for extensive software installations.

Mobile GIS is another crucial aspect of the software segment, providing field-based solutions for data collection, asset management, and real-time decision making. With the increasing use of smartphones and tablets, mobile GIS applications are becoming indispensable for sectors such as utilities, transportation, and

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

this graphs the maps was created the : https://experience.arcgis.com/experience/b296879cc1984fda833a8acc93e31476/ https://www.ncei.noaa.gov/maps/daily/

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Climate data is a vital resource for understanding and addressing the complexities of climate change. With the advent of digital technology, accessing and utilizing climate datasets has become increasingly important for researchers, policymakers, and the general public alike. In this era of data-driven decision-making, the availability of comprehensive climate datasets empowers stakeholders to analyze trends, assess risks, and develop informed strategies for climate resilience and mitigation.

The Climate Data Online platform serves as a gateway to a wealth of climate datasets, offering users the opportunity to explore, analyze, and extract valuable insights from a diverse array of environmental data sources. By providing access to a wide range of datasets encompassing various climatic variables, geographic regions, and temporal scales, Climate Data Online facilitates interdisciplinary research, fosters collaboration, and supports evidence-based decision-making in climate science and related fields.

One of the key features of Climate Data Online is its user-friendly interface, which allows users to easily navigate through different datasets and access detailed information about each dataset. By clicking on the name of a dataset, users can expand and view comprehensive descriptions, including metadata, data formats, temporal coverage, spatial resolution, and relevant links to related tools and resources. This intuitive interface enhances the usability of the platform, enabling users to quickly find and retrieve the data they need for their specific research or analysis purposes.

Moreover, Climate Data Online offers various download options, including FTP access and downloadable samples, enabling users to obtain the data in the format and resolution that best suits their requirements. Whether users need raw data for advanced analysis or pre-processed data for visualization and modeling purposes, Climate Data Online provides the flexibility and scalability to meet diverse data needs.

One of the strengths of Climate Data Online is its extensive coverage of different climatic variables, ranging from temperature and precipitation to atmospheric pressure and wind speed. By aggregating data from multiple sources, including weather stations, satellites, and climate models, Climate Data Online offers a comprehensive view of the Earth's climate system, enabling users to explore spatial and temporal patterns, identify trends, and detect anomalies.

For example, researchers studying the impact of climate change on agriculture may utilize temperature and precipitation datasets to assess changes in growing season length, drought frequency, and crop yields. Similarly, urban planners may use data on temperature and air quality to evaluate heat island effects, assess health risks, and design resilient infrastructure. By providing access to such diverse datasets, Climate Data Online facilitates interdisciplinary research and supports evidence-based decision-making across various sectors.

In addition to its rich collection of climate datasets, Climate Data Online also serves as a valuable repository of tools and resources for data analysis and visualization. From interactive maps and charting tools to statistical analysis software and programming libraries, Climate Data Online offers a variety of options for exploring and interpreting the data. Moreover, the platform provides documentation, tutorials, and user support to help users navigate the datasets and leverage the available tools effectively.

Furthermore, Climate Data Online encourages collaboration and knowledge sharing among users by facilitating community forums, workshops, and collaborative projects. By connecting researchers, practitioners, and policymakers with shared interests in climate data analysis and interpretation, Climate Data Online fosters a vibrant community of practice, where ideas are exchanged, best practices are shared, and innovative solutions are developed.

Overall, Climate Data Online plays a crucial role in advancing climate science and supporting evidence-based decision-making in response to the challenges of climate change. By providing access to comprehensive climate datasets, user-friendly tools, and a supportive community, Climate Data Online empowers stakeholders to explore, analyze, and ...

The global Geographic Information Systems (GIS) market is projected to reach a value of USD 2890.3 million by 2033, expanding at a CAGR of 5.3% during the forecast period (2025-2033). The growing demand for GIS solutions for urban planning, infrastructure management, environmental monitoring, and disaster response is driving market growth. Additionally, the increasing adoption of cloud-based GIS platforms and the integration of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) are contributing to the market's expansion. Key trends shaping the GIS market include the rise of location intelligence, which involves using GIS data to make informed decisions about spatial relationships and patterns. The increasing availability of open-source GIS software and data is also driving market growth, as it enables organizations to access and utilize GIS without significant upfront costs. Furthermore, the adoption of GIS by governments and businesses for decision-making and planning purposes is contributing to the market's expansion. Among the application segments, transport and logistics are expected to witness significant growth as GIS plays a crucial role in optimizing routes, managing fleet operations, and improving supply chain efficiency.

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,

This analysis applies a novel spatial mediation framework to examine how food retail accessibility mediates the relationship between deprivation and depression at the local level. The methodological approach combines mediation analysis principles (Judd, C.M. & Kenny, D.A., 1981) with Geographically Weighted Regression (GWR) models, allowing relationships to vary spatially across Hampshire and the Isle of Wight rather than assuming uniform effects across the region. The spatial mediation analysis involved two key steps: Step 1 established the total effect of income deprivation on depression, whilst Step 2 examined the indirect effect by modelling both deprivation and food retail accessibility as simultaneous predictors of depression. Local coefficients were then compared at each location to identify areas where food retail accessibility serves as a mediating pathway in the deprivation-depression relationship. Statistical significance was assessed using local t-values with a threshold of ±1.96 (p < 0.05), ensuring robust identification of meaningful mediation effects across different geographical contexts. The analysis utilised QOF depression prevalence data (2022), Index of Multiple Deprivation measures (2019), and Department for Transport travel time statistics to retail food outlets (2019), representing spatial access to food supply chain endpoints across the study region. Data sources: In all analyses, we used the LSOA boundaries published by the Office for National Statistics: Office for National Statistics. Census 2011 geographies [Internet]. 2020. Available from: Lower layer Super Output Areas (December 2011) https://geoportal.statistics.gov.uk/datasets/ons::lower-layer-super-output-areas-december-2011-boundaries-ew-bfc-v3/about Digital vector boundaries for Integrated Care Boards in England were those published by the Office for National Statistics: Integrated Care Boards (April 2023) EN BGC [Internet]. 2023. Available from: https://www.data.gov.uk/dataset/d6bcd7d1-0143-4366-9622-62a99b362a5c/integrated-care-boards-april-2023-en-bgc Depression Prevalence 2022 - QOF depression prevalence: Daras, K., Rose, T., Tsimpida, D., & Barr, B. (2023). Quality and Outcomes Framework Indicators: Depression prevalence (QOF_4_12) [Dataset]. University of Liverpool. Available from: https://datacat.liverpool.ac.uk/2170/ Retail accessibility: DfT. (2021). Journey time statistics, England: 2019 [Dataset]. Department for Transport. Available from: https://www.gov.uk/government/statistics/journey-time-statistics-england-2019/journey-time-statistics-england-2019#official-statistics Deprivation: McLennan, D., Noble, S., Noble, M., Plunkett, E., Wright, G., & Gutacker, N. (2019). The English indices of deprivation 2019: Technical report. Available from:https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 Longitudinal Depression: Tsimpida, D., Tsakiridi, A., Daras, K., Corcoran, R., & Gabbay, M. (2024). Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence. SSM - Population Health, 26, 101669. Available from: https://doi.org/10.1016/j.ssmph.2024.101669

https://www.youtube.com/watch?v=A8syQeFtBKc

Context

The Stanford Mass Shootings in America (MSA) is a dataset released under Creative Commons Attribution 4.0 international license by the Stanford Geospatial Center. While not an exhaustive collection of mass shootings, it is a high-quality dataset ranging from 1966 to 2016 with well-defined methodology, definitions and source URLs for user validation.

This dataset can be used to validate other datasets, such as us-mass-shootings-last-50-years, which contains more recent data, or conduct other analysis, as more information is provided.

Content

This dataset contains data by the MSA project both from it's website and from it's Github account. The difference between the two sources is only on the data format (i.e. .csv versus .geojson for the data, or .csv versus .pdf for the dictionary).

• mass_shooting_events_stanford_msa_release_06142016
  • Contains a nonexaustive list of US Mass Shootings from 1966 to 2016 in both .csv and .geojson formats.
• dictionary_stanford_msa_release_06142016
  • Contains the data dictionary in .csv and .pdf formats. Note the .pdf format provides an easier way to visualize sub-fields.

Note the data was reproduced here without any modifications other than file renaming for clarity, the content is the same as in the source.

The following sections are reproduced from the dataset creators website. For more details, please see the source.

Project background

The Stanford Mass Shootings of America (MSA) data project began in 2012, in reaction to the mass shooting in Sandy Hook, CT. In our initial attempts to map this phenomena it was determined that no comprehensive collection of these incidents existed online. The Stanford Geospatial Center set out to create, as best we could, a single point repository for as many mass shooting events as could be collected via online media. The result was the Stanford MSA.

What the Stanford MSA is

The Stanford MSA is a data aggregation effort. It is a curated set of spatial and temporal data about mass shootings in America, taken from online media sources. It is an attempt to facilitate research on gun violence in the US by making raw data more accessible.

What the Stanford MSA is not

The Stanford MSA is not a comprehensive, longitudinal research project. The data collected in the MSA are not investigated past the assessment for inclusion in the database. The MSA is not an attempt to answer specific questions about gun violence or gun laws.

The Stanford Geospatial Center does not provide analysis or commentary on the contents of this database or any derivatives produced with it.

Data collection methodology

The information collected for the Stanford MSA is limited to online resources. An initial intensive investigation was completed looking back over existing online reports to fill in the historic record going back to 1966. Contemporary records come in as new events occur and are cross referenced against a number of online reporting sources. In general a minimum of three corroborating sources are required to add the full record into the MSA (as many as 6 or 7 sources may have been consulted in many cases). All sources for each event are listed in the database.

Due to the time involved in vetting the details of any new incident, there is often a 2 to 4 week lag between a mass shooting event and its inclusion in the public release database.

It is important to note the records in the Stanford MSA span a time from well before the advent of online media reporting, through its infancy, to the modern era of web based news and information resources. Researchers using this database need to be aware of the reporting bias these changes in technology present. A spike in incidents for recent years is likely due to increased online reporting and not necessarily indicative of the rate of mass shootings alone. Researchers should look at this database as a curated collection of quality checked data regarding mass shootings, and not an exhaustive research data set itself. Independent verification and analysis will be required to use this data in examining trends in mass shootings over time.

Definition of Mass Shooting

The definition of mass shooting used for the Stanford database is 3 or more shooting victims (not necessarily fatalities), not including the shooter. The shooting must not be identifiably gang, drug, or organized crime related.

Acknowledgements

The Stanford Mass Shootings in America (MSA) is a dataset released under [Creative Commons Attribution 4.0 int...

Long term monthly mean winds which Sadler derived from his collection of aircraft data (which we have in DS365.0. [https://rda.ucar.edu/datasets/ds365.0/]) and average rawinsonde data. The aircraft data were obtained from two sources: operational GTS reports collected in Honolulu and the FHWF; and aircraft logs from many routes which often were not reported over the GTS. The aircraft winds were summarized for each month in 5-degree latitude-longitude squares. The average monthly rawinsonde data were then combined with these in manual analyses of streamlines and isotachs. The 2.5- degree grids of wind speed and direction were manually read from these.

Unless you have a special interest in this dataset, DSS recommends that you use one of our reanalysis datasets, such as ds090.2 [https://rda.ucar.edu/datasets/ds090.2/]. That's because those are based on a more complete collection of observations, improved analysis methods, and are provided in a common modern format (GRIB).