Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

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• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

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• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

Location Analytics Market Outlook

The global location analytics market size was valued at approximately USD 18.2 billion in 2023 and is projected to reach around USD 65.4 billion by 2032, growing at a robust CAGR of 15.2% during the forecast period from 2024 to 2032. This impressive growth can be attributed to the escalating demand for spatial data and analytical solutions across various industries, aiming to enhance decision-making processes and optimize business operations.

One of the primary growth factors driving the location analytics market is the increasing use of smartphones and the proliferation of Internet of Things (IoT) devices. These advancements have led to an explosion of location-based data, offering businesses the capability to analyze real-time information and make informed decisions. Moreover, the integration of location analytics with Geographic Information System (GIS) technologies has further enhanced the scope of applications, making it indispensable for sectors such as retail, transportation, and healthcare.

Another significant growth driver is the rising need for businesses to gain a competitive edge through location-based insights. Companies are leveraging location analytics to understand customer behaviors, optimize supply chains, and enhance marketing strategies. For instance, retail businesses are increasingly using location analytics to determine ideal store locations, manage inventory efficiently, and provide personalized customer experiences. Similarly, in the transportation and logistics sector, location analytics is crucial for route optimization, fleet management, and reducing operational costs.

The emergence of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also catalyzing the growth of the location analytics market. By incorporating AI and ML algorithms, location analytics solutions can offer predictive insights and trend analyses, enabling businesses to anticipate market changes and act proactively. This technological advancement is particularly beneficial for risk management and emergency response applications, where timely and accurate data is critical.

In this context, Points-of-Interest (POI) Data Solutions have emerged as a crucial component in the location analytics ecosystem. POI data provides detailed information about specific locations, such as businesses, landmarks, and other significant places, which can be leveraged by companies to enhance their spatial analysis capabilities. By integrating POI data, businesses can gain deeper insights into consumer behavior, optimize location-based services, and improve decision-making processes. This data is particularly valuable for sectors like retail and hospitality, where understanding the proximity and accessibility of various points of interest can significantly impact customer engagement and operational efficiency. As the demand for precise and comprehensive location data continues to grow, POI Data Solutions are set to play a pivotal role in advancing the capabilities of location analytics platforms.

Regionally, North America is expected to dominate the location analytics market due to the early adoption of advanced technologies and the presence of major market players in the region. Additionally, the Asia Pacific region is anticipated to witness significant growth, driven by the rapid urbanization, increasing smartphone penetration, and government initiatives promoting smart city projects. Europe is also poised for substantial growth, supported by stringent data regulations and the growing demand for spatial data analytics in various industries.

Component Analysis

The location analytics market can be segmented by component into software and services. The software segment includes tools and platforms that facilitate spatial data analysis, while the services segment encompasses consulting, integration, and maintenance services. The software segment is expected to hold a significant market share due to the increasing adoption of location analytics software solutions by enterprises to gain actionable insights from spatial data. These software solutions are designed to integrate seamlessly with existing business systems, providing users with real-time data analysis and visualization capabilities.

Location analytics software is further categorized into desktop, mobile, and web-based platforms. Desktop solutions are traditionally used for comprehensive geospati

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Geospatial Analytics Software Market Outlook

The global geospatial analytics software market size is projected to grow from USD 50.1 billion in 2023 to USD 114.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 9.5%. This remarkable growth is largely driven by the increasing adoption of geospatial technologies across various sectors, including urban planning, agriculture, transportation, and disaster management. The surge in the utilization of geospatial data for strategic decision-making, coupled with advancements in technology such as artificial intelligence (AI) and big data analytics, plays a pivotal role in propelling market growth.

One of the key growth factors of the geospatial analytics software market is the rapid digital transformation occurring globally. Governments and enterprises are increasingly recognizing the value of geospatial data in enhancing operational efficiency and strategic planning. The rise in smart city initiatives across the world has bolstered the demand for geospatial analytics, as cities leverage these technologies to optimize infrastructure, manage resources, and improve public services. Additionally, the integration of AI and machine learning with geospatial analytics has enhanced the accuracy and predictive capabilities of these systems, further driving their adoption.

Another significant driver is the growing need for disaster management and climate change adaptation. As the frequency and intensity of natural disasters increase due to climate change, there is a heightened demand for geospatial analytics to predict, monitor, and mitigate the impact of such events. Geospatial software aids in mapping hazard zones, planning evacuation routes, and assessing damage post-disaster. This capability is crucial for governments and organizations involved in disaster management and mitigation, thereby boosting the market growth.

The transportation and logistics sector is also a major contributor to the growth of the geospatial analytics software market. The advent of autonomous vehicles and the continuous evolution of logistics and supply chain management have heightened the need for precise geospatial data. Geospatial analytics enables real-time tracking, route optimization, and efficient fleet management, which are critical for the smooth operation of transportation systems. This trend is expected to continue, driving the demand for geospatial analytics solutions in transportation and logistics.

On a regional level, North America is anticipated to dominate the geospatial analytics software market, driven by technological advancements and substantial investments in geospatial technologies. The presence of major market players and the high adoption rate of advanced technologies in sectors such as defense, agriculture, and urban planning contribute to this dominance. However, the Asia Pacific region is expected to witness the highest growth rate, fueled by rapid urbanization, government initiatives for smart cities, and increasing investments in infrastructure development.

GIS Software plays a crucial role in the geospatial analytics software market, offering powerful tools for data visualization, spatial analysis, and geographic mapping. As organizations across various sectors increasingly rely on geospatial data for strategic decision-making, GIS Software provides the necessary infrastructure to manage, analyze, and interpret this data effectively. Its integration with other technologies such as AI and machine learning enhances its capabilities, enabling more accurate predictions and insights. This makes GIS Software an indispensable component for industries like urban planning, agriculture, and transportation, where spatial data is pivotal for optimizing operations and improving outcomes. The growing demand for GIS Software is a testament to its importance in driving the geospatial analytics market forward.

Component Analysis

The geospatial analytics software market is segmented into software and services when considering components. The software segment includes comprehensive solutions that integrate various geospatial data types and provide analytical tools for mapping, visualization, and data processing. This segment is expected to hold the largest market share due to the increasing adoption of these solutions in various industries for efficient data management and decision-making. The continuous advancements in software capabilities, such as the inclusion of AI and machine learning algorithms

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2032, growing at a CAGR of 12.10% during the forecast period 2026-2032.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth's surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today's interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

A major objective of plant ecology research is to determine the underlying processes responsible for the observed spatial distribution patterns of plant species. Plants can be approximated as points in space for this purpose, and thus, spatial point pattern analysis has become increasingly popular in ecological research. The basic piece of data for point pattern analysis is a point location of an ecological object in some study region. Therefore, point pattern analysis can only be performed if data can be collected. However, due to the lack of a convenient sampling method, a few previous studies have used point pattern analysis to examine the spatial patterns of grassland species. This is unfortunate because being able to explore point patterns in grassland systems has widespread implications for population dynamics, community-level patterns and ecological processes. In this study, we develop a new method to measure individual coordinates of species in grassland communities. This method records plant growing positions via digital picture samples that have been sub-blocked within a geographical information system (GIS). Here, we tested out the new method by measuring the individual coordinates of Stipa grandis in grazed and ungrazed S. grandis communities in a temperate steppe ecosystem in China. Furthermore, we analyzed the pattern of S. grandis by using the pair correlation function g(r) with both a homogeneous Poisson process and a heterogeneous Poisson process. Our results showed that individuals of S. grandis were overdispersed according to the homogeneous Poisson process at 0-0.16 m in the ungrazed community, while they were clustered at 0.19 m according to the homogeneous and heterogeneous Poisson processes in the grazed community. These results suggest that competitive interactions dominated the ungrazed community, while facilitative interactions dominated the grazed community. In sum, we successfully executed a new sampling method, using digital photography and a Geographical Information System, to collect experimental data on the spatial point patterns for the populations in this grassland community.

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors: the rising adoption of cloud-based GIS solutions offering enhanced accessibility and scalability, the escalating need for precise spatial data analysis in urban planning and resource management, and the expanding application of GIS in geological exploration for efficient resource discovery and extraction. Furthermore, advancements in location-based services (LBS) and the integration of GIS with other technologies such as IoT and AI are creating new opportunities and driving market expansion. While the market size in 2025 is estimated at $15 billion (a reasonable assumption considering similar market sizes for related technologies), the Compound Annual Growth Rate (CAGR) is projected to remain strong, likely exceeding 8% through 2033. This sustained growth indicates a highly promising market outlook for vendors and investors. However, market growth is not without challenges. High initial investment costs for sophisticated GIS software and the requirement for skilled personnel to operate and maintain these systems can pose barriers to entry, particularly for smaller organizations. Additionally, data security concerns and the need for robust data management strategies are critical factors impacting market adoption. Despite these constraints, the continued integration of GIS tools into various business processes and the growing availability of user-friendly, affordable solutions are expected to mitigate these challenges and propel the market towards sustained and significant growth in the coming years. Segmentation reveals a strong preference for cloud-based solutions due to their flexibility and cost-effectiveness, with the geological exploration and urban planning applications exhibiting the highest growth rates. Key players such as Esri, Autodesk, and Hexagon are strategically positioned to capitalize on these trends.

The compressed package (Study_code.zip) contains the code files implemented by an under review paper ("What you see is what you get: Delineating urban jobs-housing spatial distribution at a parcel scale by using street view imagery based on deep learning technique").The compressed package (input_land_parcel_with_attributes.zip) is the sampled mixed "jobs-housing" attributes data of the study area with multiple probability attributes (Only working, Only living, working and living) at the land parcel scale.The compressed package (input_street_view_images.zip) is the surrounding street view data near sampled land parcels (input_land_parcel_with_attributes.zip) with the pixel size of 240*160 obtained from Tencent map (https://map.qq.com/).The compressed package (output_results.zip) contains the result vector files (Jobs-housing pattern distribution and error distribution) and file description (Readme.txt).This project uses some Python open source libraries (Numpy, Pandas, Selenium, Gdal, Pytorch and sklearn). This project complies with the GPL license.Numpy (https://numpy.org/) is an open source numerical calculation tool developed by Travis Oliphant. Used in this project for matrix operation. This library complies with the BSD license.Pandas (https://pandas.pydata.org/) is an open source library, providing high-performance, easy-to-use data structures and data analysis tools. This library complies with the BSD license.Selenium(https://www.selenium.dev/) is a suite of tools for automating web browsers.Used in this project for getting street view images.This library complies with the BSD license.Gdal(https://gdal.org/) is a translator library for raster and vector geospatial data formats.Used in this project for processing geospatial data.This library complies with the BSD license.Pytorch(https://pytorch.org/) is an open source machine learning framework that accelerates the path from research prototyping to production deployment.Used in this project for deep learning.This library complies with the BSD license.sklearn(https://scikit-learn.org/) is an open source machine learning tool for python.Used in this project for comparing precision metrics.This library complies with the BSD license.

Mixed-methods designs, especially those in which case selection is regression-based, have become popular across the social sciences. In this paper, we highlight why tools from spatial analysis—which have largely been overlooked in the mixed-methods literature—can be used for case selection and be particularly fruitful for theory development. We discuss two tools for integrating quantitative and qualitative analysis: (1) spatial autocorrelation in the outcome of interest; and (2) spatial autocorrelation in the residuals of a regression model. The case selection strategies presented here enable scholars to systematically use geography to learn more about their data and select cases that help identify scope conditions, evaluate the appropriate unit or level of analysis, examine causal mechanisms, and uncover previously omitted variables.

The Italian geospatial analytics market, valued at €260 million in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.17% from 2025 to 2033. This expansion is driven by increasing adoption of precision agriculture techniques, the burgeoning need for efficient infrastructure management within utilities and communication sectors, and rising demand for advanced analytics in defense and intelligence applications. Furthermore, the Italian government's focus on smart city initiatives and the expanding digitalization across various sectors, including healthcare and real estate, significantly contribute to market growth. The market segmentation reveals a strong demand across diverse verticals, with agriculture, utilities, and defense exhibiting substantial growth potential. The prevalent use of surface analysis techniques reflects a focus on immediate application needs, while the growing adoption of network and geo-visualization analytics indicates a shift toward more sophisticated and insightful data interpretation. Leading players such as ESRI, Hexagon AB, and Trimble Geospatial are actively contributing to market development by providing cutting-edge software and services. Competition is likely to intensify as smaller, specialized companies emerge, offering niche solutions and catering to the evolving demands of different market segments. The forecast period (2025-2033) anticipates substantial market expansion, largely attributed to continued technological advancements, particularly in AI and machine learning, enhancing the analytical capabilities of geospatial data. However, challenges exist, potentially including data security concerns, the need for skilled professionals to interpret complex analytical results, and the high initial investment required for advanced geospatial technology implementation. Nevertheless, the long-term outlook for the Italian geospatial analytics market remains positive, driven by sustained government investment in digital infrastructure and the increasing awareness of the value proposition offered by sophisticated geospatial analysis across various sectors. The market's trajectory suggests a significant opportunity for both established and emerging players in the years to come. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

The geospatial data analytics market, currently valued at $86.39 billion in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 12.81% from 2025 to 2033. This expansion is fueled by several key factors. Increasing reliance on location intelligence across diverse sectors like agriculture (precision farming), utilities (network optimization), defense (surveillance and intelligence), and government (urban planning and resource management) is a major catalyst. Advances in technologies such as AI, machine learning, and cloud computing are enhancing the analytical capabilities of geospatial data, leading to more accurate insights and predictive modeling. Furthermore, the growing availability of high-resolution satellite imagery and sensor data is significantly expanding the data pool for analysis, contributing to market growth. The market is segmented by type (surface analysis, network analysis, geovisualization analysis) and end-user vertical, each contributing uniquely to the overall market value. Competition is fierce, with established players like ESRI, Hexagon AB, and Trimble Inc. alongside emerging technology companies vying for market share. The market's geographic distribution is expected to reflect global technological adoption rates and economic activity. North America and Europe currently hold significant market shares, but the Asia-Pacific region is projected to witness substantial growth due to increasing investments in infrastructure and technological advancements. Government initiatives promoting the use of geospatial technology in various sectors are further bolstering market expansion in developing economies. While data privacy concerns and the need for skilled professionals represent challenges, the overall market outlook remains strongly positive, underpinned by the continuous increase in data generation, sophisticated analytical tools, and the widespread acceptance of location-based services across numerous industries. The forecast period (2025-2033) anticipates a continued trajectory of expansion, with significant market penetration across a wider range of applications. Recent developments include: June 2023: Intermap Technologies leveraged its high-resolution elevation data access to perform imagery correction services for a national government organization to support the development projects in El Salvador and Honduras in Central America. In partnership with GeoSolutions, Intermap enables the creation of precision maps that are invaluable resources in supporting community safety and resiliency., March 2023: Mach9, the company building the fastest technologies for geospatial production, introduced its first product. The new product leverages computer vision and AI to produce faster 2D and 3D CAD and GIS engineering deliverables. This product launch comes amidst Mach9's pivot to a software-first business model, which is a move that is primarily driven by the rising demand for tools that accelerate geospatial data processing and analysis for infrastructure management.. Key drivers for this market are: Increase in Adoption of Smart City Development, Introduction of 5G to Boost Market Growth. Potential restraints include: Increase in Adoption of Smart City Development, Introduction of 5G to Boost Market Growth. Notable trends are: Defense and Intelligence to be the Largest End-user Industry.

According to Cognitive Market Research, the global geospatial analytics artificial intelligence market size is USD 100.5 million in 2024 and will expand at a compound annual growth rate (CAGR) of 28.60% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 40.20 million in 2024 and will grow at a compound annual growth rate (CAGR) of 26.8% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 30.15 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 23.12 million in 2024 and will grow at a compound annual growth rate (CAGR) of 30.6% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 5.03 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.0% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 2.01 million in 2024 and will grow at a compound annual growth rate (CAGR) of 28.3% from 2024 to 2031.
The remote sensing held the highest geospatial analytics artificial intelligence market revenue share in 2024.

Market Dynamics of Geospatial analytics artificial intelligence Market

Key Drivers for Geospatial analytics artificial intelligence Market

Advancements in AI and Machine Learning to Increase the Demand Globally

The global demand for geospatial analytics is significantly driven by advancements in AI and machine learning, technologies that are revolutionizing how spatial data is analyzed and interpreted. As AI models become more sophisticated, they enhance the capability to automate complex geospatial data processing tasks, leading to more accurate and insightful analyses. Machine learning, particularly, enables systems to improve their accuracy over time by learning from vast datasets of geospatial information, including satellite imagery and sensor data. This leads to more precise predictions and better decision-making across multiple sectors such as environmental management, urban planning, and disaster response. The integration of AI with geospatial technologies not only improves efficiency but also opens up new possibilities for innovation, making it a critical driver for increased global demand in the geospatial analytics market.

Government Initiatives and Support for Smart Cities to Propel Market Growth

Government initiatives supporting the development of smart cities are propelling the growth of the geospatial analytics market. As urban areas around the world transform into smart cities, there is a significant increase in demand for advanced technologies that can analyze and interpret geospatial data to enhance urban planning, infrastructure management, and public safety. Geospatial analytics, powered by AI, plays a crucial role in these projects by enabling real-time data processing and insights for traffic control, utility management, and emergency services coordination. These technologies ensure more efficient resource allocation and improved quality of urban life. Government funding and policy support not only validate the importance of geospatial analytics but also stimulate innovation, attract investments, and foster public-private partnerships, thus driving the market forward and enhancing the capabilities of smart city initiatives globally.

Restraint Factor for the Geospatial analytics artificial intelligence Market

Complexity of Data Integration to Limit the Sales

The complexity of data integration poses a significant barrier to the adoption and effectiveness of geospatial analytics AI systems, potentially limiting sales in this market. Geospatial data, inherently diverse and sourced from various collection methods like satellites, UAVs, and ground sensors, comes in multiple formats and resolutions. Integrating such disparate data into a cohesive, usable format for AI analysis is a challenging process that requires advanced data processing tools and expertise. This complexity not only increases the time and costs associated with project implementation but also raises the risk of errors and inefficiencies in data analysis. Furthermore, the difficulty in achieving seamless integration can deter organizations, particularly those with limited IT capabilities, from investing in geospatial analytics solutions. Overcoming these integration challenges is crucial for enabl...

Location Intelligence Analytics Market Outlook

The global location intelligence analytics market size is projected to grow from USD 14.2 billion in 2023 to USD 31.7 billion by 2032, exhibiting a CAGR of approximately 9.4% during the forecast period. This robust growth is primarily driven by the increasing demand for spatial data and analytical tools across various industries to enhance decision-making processes and optimize business operations. As organizations increasingly recognize the value of location-based insights, they are investing in sophisticated analytics solutions that leverage geographic data to drive business outcomes and gain competitive advantages.

One of the primary growth factors for the location intelligence analytics market is the proliferation of IoT devices and the consequent surge in location-based data generation. With billions of connected devices expected to be operational in the coming years, the volume of location-specific data is set to explode. Businesses across industries are eager to harness this data to gain insights into consumer behavior, improve operational efficiency, and develop targeted marketing strategies. Moreover, advancements in AI and machine learning are enabling more sophisticated analysis of location data, providing deeper insights and predictive capabilities that are invaluable to enterprises.

Another significant driver for market growth is the growing adoption of smart city initiatives across the globe. Governments and municipalities are increasingly implementing location intelligence solutions to enhance urban planning, traffic management, and public safety. By leveraging location-based analytics, cities can optimize resource allocation, improve citizen services, and drive sustainable development. Furthermore, the integration of real-time data from various sources, such as sensors and social media, with geographic information systems (GIS) is facilitating more dynamic and responsive urban management systems, thus propelling the demand for location intelligence analytics.

The increasing emphasis on business intelligence and data-driven decision-making is also fueling the demand for location intelligence analytics. In today's competitive landscape, organizations are seeking to leverage every bit of data to gain actionable insights and stay ahead. Location intelligence provides a unique perspective by overlaying geographic data on traditional business data, offering a holistic view of trends and patterns. This capability is particularly valuable in sectors such as retail, transportation, and logistics, where location-based insights can directly impact revenue generation, cost savings, and customer satisfaction.

Regionally, North America is expected to hold the largest share of the location intelligence analytics market, driven by the presence of major technology companies and the rapid adoption of advanced analytics solutions across industries. The region's commitment to innovation and technological advancement is further supported by substantial investments in R&D activities. Additionally, Europe is anticipated to witness significant growth, influenced by stringent regulatory frameworks and a heightened focus on data privacy and security. In contrast, the Asia Pacific region is projected to demonstrate the highest growth rate, attributed to the rapid digital transformation and increasing investments in smart city projects across emerging economies like India and China.

Component Analysis

The location intelligence analytics market is broadly segmented into software and services. Software solutions are a critical component of this market, offering the necessary tools and platforms for collecting, analyzing, and visualizing geographic data. These software solutions are designed to process large volumes of spatial data, integrate various data sources, and provide users with intuitive and interactive interfaces for data exploration. The advancements in cloud computing and the increasing adoption of Software as a Service (SaaS) models are further driving the demand for location intelligence software, as they offer greater scalability, flexibility, and cost-effectiveness to organizations of all sizes.

Within the software segment, Geographic Information System (GIS) solutions are particularly prominent. GIS technology enables the mapping and analysis of spatial data, allowing users to visualize relationships, patterns, and trends in complex datasets. The ability to integrate GIS with other enterprise systems, such as Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP), enhances its ut

Spatial interpolation is critical in geographic information systems (GIS) and environmental science, particularly when dealing with high-dimensional and nonlinear data. Classical methods like Kriging and inverse distance weighting (IDW) often struggle with the complexities of irregular terrain and sparse datasets, and are inadequate for capturing the nonlinear characteristics of high-dimensional spatial data. In this paper, we introduce a novel interpolation method based on the Denoising Diffusion Probabilistic Model (DDPM), which incorporates ConvNeXt V2 blocks within a UNet architecture. To validate the performance of our model, we employ the Copernicus Digital Elevation Model (COP-DEM) dataset for simulation experiments. Experimental results demonstrate that the proposed DDPM method significantly outperforms classical interpolation techniques, particularly in scenarios with high-density control points, producing high-quality interpolation results with strong transferability. This approach shows considerable promise for spatial interpolation in high-dimensional, complex terrains, offering a more robust alternative to traditional methods. It not only addresses key challenges in interpolation accuracy but also opens up new possibilities for applying generative models in other spatial data processing domains, including environmental monitoring and geospatial modeling.

It then proceeds to present a quick overview of how geospatial data, methods and tools are playing key roles in the measurement, analysis and modelling of climate change and its effects on human health. Geospatial techniques are proving indispensable for making more accurate assessments and estimates, predicting future trends more reliably, and devising more optimised climate change adaptation and mitigation plans.Link: https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-023-00324-9

Geographic Information System (GIS) Analytics Market Outlook

The global Geographic Information System (GIS) Analytics market size is projected to grow remarkably from $9.1 billion in 2023 to $21.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 10.2% during the forecast period. This substantial growth can be attributed to several factors such as technological advancements in GIS, increasing adoption in various industry verticals, and the rising importance of spatial data for decision-making processes.

The primary growth driver for the GIS Analytics market is the increasing need for accurate and efficient spatial data analysis to support critical decision-making processes across various industries. Governments and private sectors are investing heavily in GIS technology to enhance urban planning, disaster management, and resource allocation. With the world becoming more data-driven, the reliance on GIS for geospatial data has surged, further propelling its market growth. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) with GIS is revolutionizing the analytics capabilities, offering deeper insights and predictive analytics.

Another significant growth factor is the expanding application of GIS analytics in disaster management and emergency response. Natural disasters such as hurricanes, earthquakes, and wildfires have highlighted the importance of GIS in disaster preparedness, response, and recovery. The ability to analyze spatial data in real-time allows for quicker and more efficient allocation of resources, thus minimizing the impact of disasters. Moreover, GIS analytics plays a pivotal role in climate change studies, helping scientists and policymakers understand and mitigate the adverse effects of climate change.

The transportation sector is also a major contributor to the growth of the GIS Analytics market. With the rapid urbanization and increasing traffic congestion in cities, there is a growing demand for effective transport management solutions. GIS analytics helps in route optimization, traffic management, and infrastructure development, thereby enhancing the overall efficiency of transportation systems. The integration of GIS with Internet of Things (IoT) devices and sensors is further enhancing the capabilities of traffic management systems, contributing to the market growth.

Regionally, North America is the largest market for GIS analytics, driven by the high adoption rate of advanced technologies and significant investment in geospatial infrastructure by both public and private sectors. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to the rapid urbanization, infrastructural developments, and increasing government initiatives for smart city projects. Europe and Latin America are also contributing significantly to the market growth owing to the increasing use of GIS in urban planning and environmental monitoring.

Component Analysis

The GIS Analytics market can be segmented by component into software, hardware, and services. The software segment holds the largest market share due to the continuous advancements in GIS software solutions that offer enhanced functionalities such as data visualization, spatial analysis, and predictive modeling. The increasing adoption of cloud-based GIS software solutions, which offer scalable and cost-effective options, is further driving the growth of this segment. Additionally, open-source GIS software is gaining popularity, providing more accessible and customizable options for users.

The hardware segment includes GIS data collection devices such as GPS units, remote sensing instruments, and other data acquisition tools. This segment is witnessing steady growth due to the increasing demand for high-precision GIS data collection equipment. Technological advancements in hardware, such as the development of LiDAR and drones for spatial data collection, are significantly enhancing the capabilities of GIS analytics. Additionally, the integration of mobile GIS devices is facilitating real-time data collection, contributing to the growth of the hardware segment.

The services segment encompasses consulting, implementation, training, and maintenance services. This segment is expected to grow at a significant pace due to the increasing demand for professional services to manage and optimize GIS systems. Organizations are seeking expert consultants to help them leverage GIS analytics for strategic decision-making and operational efficiency. Additionally, the growing complexity o

The ANZ Geospatial Analytics market, valued at $0.68 million in 2025, is projected to experience robust growth, driven by increasing adoption across diverse sectors. A Compound Annual Growth Rate (CAGR) of 9.13% from 2025 to 2033 indicates a significant expansion potential. Key drivers include the rising demand for precise location intelligence in agriculture (precision farming), utility management (network optimization), and infrastructure development (real estate and construction). Furthermore, advancements in data analytics techniques, particularly AI and machine learning, are enhancing the capabilities of geospatial analytics, leading to more insightful data analysis and improved decision-making. The market segmentation reveals strong demand across various verticals, with agriculture, utilities, and defense & intelligence leading the way. While data limitations prevent precise regional breakdowns for ANZ, the global trend suggests a significant market presence in Australia and New Zealand, supported by robust government initiatives and private sector investments in digital infrastructure. The presence of established players like CoreLogic NZ Limited and Esri Australia, alongside emerging innovative companies, contributes to the market’s dynamism and future potential. The forecast period (2025-2033) presents substantial opportunities for market expansion, particularly as businesses increasingly recognize the strategic value of location-based insights. Government initiatives promoting the use of geospatial data for better resource management, infrastructure planning, and disaster response are further catalyzing market growth. Challenges include data security concerns, integration complexities across different platforms, and the need for skilled professionals to handle and interpret geospatial data. However, the overall market outlook remains positive, fueled by ongoing technological advancements and a growing awareness of the benefits derived from geospatial analytics in driving operational efficiency and informed decision-making across diverse industry sectors within Australia and New Zealand. This report provides a detailed analysis of the Australia and New Zealand (ANZ) geospatial analytics market, offering invaluable insights for businesses operating or planning to enter this dynamic sector. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report offers a comprehensive overview of market size, trends, and future projections, valued in millions. The report leverages historical data (2019-2024) to paint a robust picture of market evolution. Recent developments include: January 2023: Ecopia AI (Ecopia) and Woolpert announced an expanded collaboration to map Australia's top metropolitan areas in 3D. The resulting vector maps will offer Woolpert's Asia-Pacific clients an accurate, detailed, and up-to-date foundational layer of geospatial data representing the dimensional world. As one of the leading geospatial services providers, Woolpert works with commercial and government organizations alike to map and analyze locations for strategic decision-making., September 2022: Wellington-based Geospatial data, technology, and analytics company Lynker Analytics announced that it had been selected by Toitū Te Whenua Land Information New Zealand in order to capture the building outlines from publicly owned aerial imagery over the next three years. Toitū Te Whenua Land Information New Zealand maintains a national open dataset of the building outlines extracted from multiple years of imagery captured through airborne sensors.. Key drivers for this market are: Growing Demand for Geospatial Analytics in Smart City Development and Urban Planning, Integration of Advanced Technologies such as AI and ML in Geospatial Analytics Solutions. Potential restraints include: Higher Costs Associated With Geospatial Analytics Solutions. Notable trends are: Agriculture Segment is Anticipated to Hold Significant Market Share.

The Russia Geospatial Analytics market, valued at an estimated $XX million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.84% from 2025 to 2033. This expansion is fueled by several key drivers. Firstly, increasing government initiatives focused on infrastructure development and modernization are creating a significant demand for advanced geospatial analysis. Secondly, the burgeoning adoption of precision agriculture techniques within the agricultural sector is driving the utilization of geospatial technologies for optimized land management and resource allocation. Furthermore, the expanding telecommunications and utility sectors are leveraging geospatial analytics for network optimization, asset management, and improved service delivery. The defense and intelligence sectors also contribute significantly to market growth through the adoption of advanced geospatial intelligence capabilities for surveillance and strategic planning. Key market segments include surface analysis, network analysis, and geovisualization, with applications spanning across agriculture, utilities, defense, government, mining, transportation, healthcare, and real estate. Competitive landscape is comprised of both established international players like Hexagon AB and local companies such as AGR Software Russia, reflecting a mix of global expertise and localized market understanding. However, market growth may encounter certain restraints. The high initial investment costs associated with implementing geospatial analytics systems can pose a barrier to entry for smaller companies. Additionally, the complexity of the technology and the need for specialized expertise may limit widespread adoption across all sectors. Despite these challenges, the long-term outlook for the Russia Geospatial Analytics market remains positive, underpinned by ongoing technological advancements, government support for digital transformation, and the increasing recognition of the value of geospatial data in various industries. The market's segmentation offers ample opportunities for specialized service providers and technology companies catering to the specific needs of different sectors, fostering innovation and further driving market expansion. Recent developments include: February 2023: Russia planned to add two Glonass-K satellites and one Glonass-K2 to the constellation in 2023, confirmed by Glavkosmos, a Russian space agency Roscosmos subsidiary. This increased investment in satellite constellations in the country would ease the data access for the geospatial data analytic market vendors and shows the market potential for the geospatial analytics market in the country., December 2022: Ethiopia and Russia partnered to increase their bilateral cooperation in various sectors, including geospatial and space science, in the 8th Ethio-Russia Intergovernmental Commission meeting, which has been planned to improve trade, investment, and scientific and technical cooperation and advance and transform people-to-people relations. This collaboration can increase the Russian government agency's demand for geospatial data analytics to address Ethiopia's geospatial data analysis needs, creating an opportunity for market vendors in Russia.. Key drivers for this market are: Increase in Adoption of Smart City Development, Implementation of 5G technology in The Country Supports the Market Growth. Potential restraints include: Increase in Adoption of Smart City Development, Implementation of 5G technology in The Country Supports the Market Growth. Notable trends are: Increase in Adoption of Smart City Development in The Country Drives the Market.

The ASEAN Geospatial Analytics Market, valued at approximately $0.14 Billion USD in 2025 (estimated based on the global market size of $0.68 Billion and considering ASEAN's significant share of the Asia-Pacific region), is poised for robust growth, projected to achieve a Compound Annual Growth Rate (CAGR) of 12.08% from 2025 to 2033. This expansion is fueled by several key drivers: increasing government investments in infrastructure development and smart city initiatives, rising adoption of precision agriculture techniques requiring advanced geospatial analysis, and the growing need for effective disaster management and environmental monitoring solutions across the region. Furthermore, the burgeoning telecommunications sector and the expanding use of IoT devices are generating substantial volumes of location-based data, providing ample fuel for the geospatial analytics market. Technological advancements, such as the proliferation of cloud-based GIS platforms and the enhanced capabilities of AI and machine learning algorithms in geospatial data processing, are further accelerating market growth. While data privacy concerns and the need for skilled professionals pose some challenges, the overall market outlook remains highly positive, indicating significant opportunities for businesses operating within the ASEAN geospatial analytics landscape. The market segmentation reveals significant opportunities within various sectors. Agriculture, with its dependence on precise land management and yield optimization, represents a substantial segment. Similarly, the utility and communication sectors are increasingly leveraging geospatial analytics for network optimization and infrastructure planning. Government agencies are key adopters for urban planning, resource management, and public safety applications. The defense and intelligence sector's use of geospatial intelligence for strategic decision-making also contributes significantly. While North America and Europe currently hold larger market shares globally, the ASEAN region's rapid economic development and increasing digitalization efforts suggest a trajectory of rapidly increasing market share within the coming decade. The presence of several key players, including both international and regional companies, signifies a competitive but dynamic market environment fostering innovation and service diversification. Recent developments include: March 2023: The Singapore Land Authority (SLA) announced a Memorandum of Understanding (MOU) with the Container Depot and Logistics Association (Singapore), or CDAS, to improve the routing of heavy vehicles. Under the two-year partnership, CDAS and SLA will utilize geospatial data such as road conditions and restrictions to enable the CDAS's member companies to route their heavy vehicles efficiently and safely., August 2022: Thaicom Public Company Limited, one of Asia's leading satellite operators, signed a strategic partnership agreement with US-based Orbital Insight Inc., a geospatial analytics and location intelligence technology company, to enable Thaicom to deliver leading data insights and intelligence services leveraging space-based technology and other sources with Orbital Insight Inc.'s geospatial analytics platform to its consumers in Thailand and the region.. Key drivers for this market are: Increase in Demand for Geospatial Analytics in Smart City Development and Urban Planning, Growth in 5G Deployment to Boost the Market Growth. Potential restraints include: Increase in Demand for Geospatial Analytics in Smart City Development and Urban Planning, Growth in 5G Deployment to Boost the Market Growth. Notable trends are: Agriculture Segment is Anticipated to Witness Significant Traction.

Ready for something more complex? An easy-to-follow guide to writing specialized Python scripts for spatial data in ArcGlS Pro.

Advanced Python Scripting for ArcGlS Pro builds on Python Scripting for ArcGlS Pro (Esri Press, 201 9). Learn how to create a geoprocessing tool out of your script and automate tasks in ArcGlS Pro, how to share your tools with others, as well as master a number of more specialized tasks.Some of the key topics you will learn include:Python toolboxesCreating and sharing script toolsCreating functions and classesManaging Python packages and environmentsArcPy and ArcGlS API for PythonJupyter Notebook, PandasNumPy, MatplotlibMigrating scripts from Python 2 to 3Helpful "points to remember," key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Companion data and exercises are available online.Advanced Python Scripting for ArcGlS Pro is perfect for more experienced developers who are lookilngto upgrade their skills.Don't forget to also check out Esri Press's other Python title: Python Scripting for ArcGlS ProAUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPaul A. Zandbergen is an associate professor of geography at the University of New Mexico in Albuquerque. His areas of expertise include geographic information science; spatial and statistical analysis techniques using GIS; error and uncertainty in spatial data; GIS applications in criminology, economics, health, and spatial ecology; terrain analysis and modeling; and community-based mapping using GIS and GPS.Pub Date: print: 7/14/2020 Digital: 7/14/2020ISBN: print: 9781589486188 Digital: 9781589486195Price: print: $69.99 USD Digital: $69.99 USDPages: 300 Trim: 8 x 1 0 in.Table of ContentsPrefaceAcknowledgmentsChapter 1. Creating Python Functions and ClassesChapter 2. Creating Python script toolsChapter 3. Python toolboxesChapter 4. Sharing toolsChapter 5. Managing Python packages and environmentsChapter 6. Essential Python Modules and Packages for GeoprocessingChapter 7. Migrating Scripts from Python 2 to 3 Chapter 8. ArcGlS API for PythonIndexPython Scripting and Advanced Python Scripting for ArcGIS Pro | Official Trailer | 2020-07-12 | 01:04Paul Zandbergen | Interview with Esri Press | 2020-07-10 | 25:37 | Link.