Spatial Data Science

 

Spatial Data Science

Spatial Data Science will show GIS scientists and practitioners how to add and use new analytical methods from data science in their existing GIS platforms. By explaining how the spatial domain can provide many of the building blocks, it's critical for transforming data into information, knowledge, and solutions.

"Spatial Data Science" is a specialized guide that delves into the intersection of spatial data and data science, focusing on analyzing, visualizing, and interpreting geospatial data. This book is tailored for professionals, researchers, and students who are interested in leveraging spatial data to solve real-world problems across various domains such as urban planning, environmental science, transportation, and business analytics.

Key Features of the Book

Comprehensive Introduction to Spatial Data

Covers fundamental concepts of spatial data, including coordinate systems, spatial relationships, and geographic data types (raster and vector).

Focus on Analytical Tools

Explores tools and libraries like:

Python: GeoPandas, Shapely, Folium, and Rasterio.

R: sf, sp, and tmap.

Demonstrates integration with GIS software such as QGIS and ArcGIS.

Real-World Applications

Case studies and projects focus on topics like mapping, geospatial machine learning, urban development analysis, and environmental modeling.

Visualization Techniques

Guides readers in creating compelling maps and interactive visualizations using tools like Matplotlib, Plotly, and Leaflet.

Advanced Topics

Covers spatial statistics, geostatistics, spatial interpolation, and network analysis, catering to advanced learners.

Who Should Read This Book?

Data Scientists and Analysts: Those looking to expand their expertise into spatial data applications.

GIS Professionals: Individuals interested in applying data science techniques to geospatial data.

Academics and Researchers: Useful for students and researchers in geography, environmental science, and related fields.

Urban Planners and Policymakers: Leverage spatial insights for decision-making and policy development.

Why It Stands Out

Interdisciplinary Approach: Combines spatial thinking with data science methodologies.

Practical Orientation: Emphasizes hands-on learning with examples and exercises.

Wide Applicability: Showcases how spatial data science impacts diverse fields, from disaster management to business intelligence.

This book is for those using or studying GIS and the computer scientists, engineers, statisticians, and information and library scientists leading the development and deployment of data science.

Hard Copy: Spatial Data Science

Kindle: Spatial Data Science