Geospatial Data Science Quick Start Guide电子书

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Preface

Who this book is for

What this book covers

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Introducing Location Intelligence

Location data

Understanding location data from various perspectives

From a business perspective

From a technical perspective

From a data perspective

Types of location data

Location data intelligence

Application of location data intelligence

User or customer perspective

Venue or business perspective

Location data science versus data science

Data science

Location (spatial) data science

A primer on Google Colaboratory and Jupyter Notebooks

Summary

Consuming Location Data Like a Data Scientist

Exploratory data analysis

Handling missing values

Handling time values

Time values as a feature

Handling unrelated data

Spatial data processing

Taxi zones in New York

Visualization of taxi zones

Spatial joins

Calculating distances

Haversine distance

Manhattan distance

Error metric

Interpreting errors

Building the model

Validation data and error metrics

Summary

Performing Spatial Operations Like a Pro

GeoDataFrames and geometries

Geographic coordinates and geometries

Accessing the data

Geometry

Coordinate reference systems

GeoDataFrames

Spatial operations

Projections

Buffer analysis

Spatial joins

Location data visualization

Summary

Making Sense of Humongous Location Datasets

K-means clustering

The crime dataset

Cleaning data

Converting into a GeoDataFrame

K-means clustering with scikit-learn

Density-Based Spatial Clustering Applications with Noise

Detecting outliers

Detecting clusters

Spatial autocorrelation

Points in a polygon

Global spatial autocorrelation

The choropleth map

Spatial similarity and spatial weights

Global spatial autocorrelation

Local spatial autocorrelation

Summary

Nudging Check-Ins with Geofences

Geofencing

Geofencing applications

Marketing and geofencing

Geometry and topology (lines and polygons)

Line geometries

Polygon geometries

Topology – points in a polygon

Geofencing with Plotly

Masking

Plotly interactive maps

Summary

Let's Build a Routing Engine

Fundamentals of graph data structure

Directional graphs

Weighted graphs

Shortest path analysis on a simple graph

Dijkstra's algorithm

Calculating Dijkstra's shortest path

Calculating Dijkstra shortest path length

Calculating single source Dijkstra path length

Turning a simple DataFrame into graphs

Building a graph based on a road network

Open Street Maps data

Exploring the road data

Creating a graph from a DataFrame

Reading and exploding the geometry

Calculating the distance of edges

Finding a proxy for maximum speed

Accounting for directionality

Calculating drivetime

Building the graph

Shortest path analyses on the road network graph

Dijkstra's shortest path analysis

Dijkstra's shortest path cost

Single source Dijkstra's shortest path cost

Concept of isochrones

Constructing an isochrone

Summary

Getting Location Recommender Systems

Exploratory data analysis

Rating data

Restaurants data

Recommender systems

KNNWithMeans

SVDpp

Comparison and interpretations

Location-based recommenders

Summary

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