In spatial data science, things in closer proximity to one another likely have more in common than things that are farther apart. With this practical book, geospatial professionals, data scientists, business analysts, geographers, geologists, and others familiar with data analysis and visualization will learn the fundamentals of spatial data analysis to gain a deeper understanding of their data questions.Author Bonny P. McClain demonstrates why detecting and quantifying patterns in geospatial data is vital. Both proprietary and open source platforms allow you to process and visualize spatial information. This book is for people familiar with data analysis or visualization who are eager to explore geospatial integration with Python.This book helps you:-Understand the importance of applying spatial relationships in data science-Select and apply data layering of both raster and vector graphics-Apply location data to leverage spatial analytics-Design informative and accurate maps-Automate geographic data with Python scripts-Explore Python packages for additional functionality-Work with atypical data types such as polygons, shape files, and projections-Understand the graphical syntax of spatial data science to stimulate curiosityDr. Bonny P McClain is a member of the National Press Club, 500 Women Scientists, and Investigational Reporters and Editors allowing access to a wide variety of health policy and health economic discussions. Bonny applies advanced data analytics including data engineering and geoenrichment to discussions of poverty, race, and gender. Her research targets judgementsabout social determinants, racial equity, and elements of intersectionality to illuminate the confluence of metrics contributing to poverty. Moving beyond zipcodes to explore apportioned socioeconomic data based on underlying population data leads to discovering novel variables based on location to build more context to complex data questions.In order to influence change or pathways to mitigate factors contributing to “poverty” we need to evaluate the measures that influence the social context. Core themes of racism, class exploitation, sexism and nationalism and heterosexism all contribute to social inequality. Professionally and personally she redefines how we measure these attributes and how we can more accurately identify factors amenable to intervention. Spatial data hosts a variety of physical and cultural features to reveal distribution patterns helping analysts and data professionals understand underlying causes of these patterns. The ability to query these relationships can inform policy and identify solutions.Bonny is a Tableau User Group Leader, Tableau Speaker’s Bureau member and Data Analytics Professional. Her professional goals include working to improve data literacy through education, Tableau skill integration, as well as R, Python, and Tableau Prep tools, exploring large datasets and curating empathetic answers to larger questions--making a big world seem smaller. Table of contents1. Introduction to Geospatial AnalyticsConceptual Framework for Spatial Data SciencePlaces as Objects (Points, Lines, and Polygons)Evaluating and Selecting Data2. Essential Facilities for Spatial AnalysisUnderstanding Spatial RelationshipsSpatial LiteracyMapping InequalitiesData Resources3. QGIS: Python for Spatial AnalyticsExploring the QGIS workspaceThe Python pluginAccessing the dataWorking with layer panelsWeb Feature Service (WFS)Discovering attributesSummaryResources4. Geospatial Analytics in the Cloud: Google Earth Engine and Other ToolsWhy Geospatial Analytics in the Cloud?Using the GEE Code Editor and GeemapSetup and InstallationCreating a Conda EnvironmentNavigating GeemapBasemapsLANDSAT 9The National Land Cover Database BasemapAccessing the DataBuilding a custom legendLeafmap: An Alternative to Google Earth EngineSummaryResources5. OpenStreetMap: Accessing Geospatial Data with OSMnxTagsA Conceptual Model of Open Street MapInstalling OSMnxChoosing a location or placeExplore the Code to Understand ArgumentsCalculating Travel TimesBasic Statistical MeasuresCustomizing Your Neighborhood MapsGeometries from placeGeometries from addressConclusion6. ArcGIS Python APIHow does the ArcGIS Python API work?Installing ArcGIS API and Python Distribution with CondaConnecting to the ArcGIS Python APIExploring Imagery Layers: Urban Heat Island MapsRaster functionsExploring Image AttributesImproving ImagesComparing a location over multiple points in timeFiltering layersConclusion7. Geopandas and spatial statisticsInstalling GeopandasWorking with GeoJSON filesCreating a GeoDataFrameWorking with US Census Data and Cenpy: Washington, DC, Demographic MapThe Python Spatial Analysis Library: Comparing Urban Segregation of Hispanic Populations in Two CitiesTool TipConclusionAbout the Author