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681.3*H4 --- Information systems applications (GIS etc.) --- Geodatabases --- 681.3*H4 Information systems applications (GIS etc.) --- Digital geographical databases --- GDBs (Geodatabases) --- Geographic databases --- Geographic information databases --- Geographic information system databases --- Geographic information systems databases --- Geographical databases --- Geographical information system databases --- Geographical information systems databases --- Geography --- GIS databases --- GIS geodatabases --- Geographic information systems --- Geoinformatics --- Object-oriented databases --- Relational databases --- Databases

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Geographic information systems. --- Geodatabases. --- Geodatabases --- Geographic information systems --- Geographical information systems --- GIS (Information systems) --- Information storage and retrieval systems --- Digital geographical databases --- GDBs (Geodatabases) --- Geographic databases --- Geographic information databases --- Geographic information system databases --- Geographic information systems databases --- Geographical databases --- Geographical information system databases --- Geographical information systems databases --- Geography --- GIS databases --- GIS geodatabases --- Geoinformatics --- Object-oriented databases --- Relational databases --- Databases

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Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn Understand the fundamental concepts of working with geodata Work with multiple geographical data types and file formats in Python Create maps in Python Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment.

Machine learning. --- Python (Computer program language) --- Geodatabases. --- Scripting languages (Computer science) --- Digital geographical databases --- GDBs (Geodatabases) --- Geographic databases --- Geographic information databases --- Geographic information system databases --- Geographic information systems databases --- Geographical databases --- Geographical information system databases --- Geographical information systems databases --- Geography --- GIS databases --- GIS geodatabases --- Geographic information systems --- Geoinformatics --- Object-oriented databases --- Relational databases --- Learning, Machine --- Artificial intelligence --- Machine theory --- Databases

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Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn Understand the fundamental concepts of working with geodata Work with multiple geographical data types and file formats in Python Create maps in Python Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment.

Python (Computer program language) --- Object-oriented programming (Computer science) --- Computer programming --- Object-oriented methods (Computer science) --- Document Object Model (Web site development technology) --- Scripting languages (Computer science) --- Machine learning. --- Geodatabases. --- Digital geographical databases --- GDBs (Geodatabases) --- Geographic databases --- Geographic information databases --- Geographic information system databases --- Geographic information systems databases --- Geographical databases --- Geographical information system databases --- Geographical information systems databases --- Geography --- GIS databases --- GIS geodatabases --- Geographic information systems --- Geoinformatics --- Object-oriented databases --- Relational databases --- Learning, Machine --- Artificial intelligence --- Machine theory --- Databases

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With the help of the Internet and accompanying tools, creating and publishing online maps has become easier and rich with options. A city guide web site can use maps to show the location of restaurants, museums, and art venues. A business can post a map for reaching its offices. The state government can present a map showing average income by area. Developers who want to publish maps on the web often discover that commercial tools cost too much and hunting down the free tools scattered across Internet can use up too much of your time and resources. "Web Mapping Illustrated shows you how to create maps, even interactive maps, with free tools, including MapServer, OpenEV, GDAL/OGR, and PostGIS. It also explains how to find, collect, understand, use, and share mapping data, both over the traditional Web and using OGC-standard services like WFS and WMS. Mapping is a growing field that goes beyond collecting and analyzing GIS data. "Web Mapping Illustrated shows how to combine free geographic data, GPS, and data management tools into one resource for your mapping information needs so you don't have to lose your way while searching for it. Remember the fun you had exploring the world with maps? Experience the fun again with "Web Mapping Illustrated. This book will take you on a direct route to creating valuable maps.

Cartographie --- cartography --- Internet --- Logiciel --- Computer software --- Digital mapping. --- Web site development. --- Geografie --- Geografische Informatie Systemen --- Algemeen. --- Web site development --- Sites Web --- Développement --- Digital mapping --- Geodatabases --- Development of Web sites --- Web sites --- Internet programming --- Digital geographical databases --- GDBs (Geodatabases) --- Geographic databases --- Geographic information databases --- Geographic information system databases --- Geographic information systems databases --- Geographical databases --- Geographical information system databases --- Geographical information systems databases --- Geography --- GIS databases --- GIS geodatabases --- Geographic information systems --- Geoinformatics --- Object-oriented databases --- Relational databases --- Computer cartography --- Computer mapping --- Digitized mapping --- Cartography --- Design and construction --- Development --- Databases --- Gis