A Beginner‘s Guide to Geospatial Data Analysis

What is Geospatial Data Analysis?

At its core, geospatial data analysis is all about understanding and extracting insights from data that has a geographic or locational component. Geospatial data (also known as spatial data) is any data that has information about its location on the surface of the Earth, whether in the form of coordinates, address, city name, and so on. This location information adds an important dimension to the data and allows us to analyze how things relate spatially.

So in short, geospatial data analysis involves applying analytical methods, algorithms and tools to explore, visualize and interpret geospatial data in order to discover relationships, patterns and trends. It allows us to ask and answer questions like:

• How are things distributed geographically?
• What spatial relationships exist between different entities?
• How have spatial patterns changed over time?
• Where are the optimal locations to site a new facility?

Importance and Applications

The power of location is immense and geospatial data analysis has applications across almost every domain and industry you can think of. Some prominent use cases include:

• Urban planning and smart cities – e.g. planning urban infrastructure, transport networks, green spaces
• Environmental monitoring – e.g. tracking deforestation, species habitats, impacts of climate change
• Disaster management – e.g. mapping vulnerable areas, planning evacuation routes, coordinating emergency response
• Public health – e.g. tracking spread of infectious diseases, finding sources of pollution, improving access to healthcare
• Asset and utility management – e.g. managing pipelines, power lines, telecommunication networks
• Logistics and transportation – e.g. optimizing delivery routes, analyzing passenger commute patterns
• Geo-marketing – e.g. finding optimal locations for retail stores, targeted localized advertising

For businesses and organizations, geospatial analysis provides a powerful tool to incorporate a geographical perspective into decision making. It can deliver location intelligence to optimize real estate, enhance customer experiences, improve operational efficiency and reduce costs.

Key Concepts

Before diving deeper into geospatial analysis, let‘s review some fundamental concepts:

Coordinate Reference Systems
In order to locate something on the Earth‘s roughly spherical surface on a flat map, we need a coordinate reference system (CRS). A CRS defines how a 2D or 3D coordinate maps to a real location on the Earth‘s surface. There are two main types of CRS:

• Geographic coordinate systems that use angular units (latitude and longitude) to define locations on a model of the Earth as an ellipsoid or geoid. A common example is WGS84.

• Projected coordinate systems that use linear units (feet, meters, etc.) to define locations on a flat surface using map projections like Mercator, UTM etc. Projections inevitably introduce distortions compared to the true shape of the Earth.

Vector and Raster Data Models
Geospatial data is commonly represented using two data models:

• Vector data model uses points, lines and polygons (areas) to represent discrete geometric entities. For example, a building footprint can be represented as a polygon, a road as a line, and a tree as a point. Vector data is good for representing discrete, well-defined features.

• Raster data model divides the area of interest into a regular grid of cells (pixels) and assigns a value to each cell. Satellite imagery and digital elevation models are examples of raster data. Rasters are suitable for representing continuous fields and imagery.

Spatial Databases
While geospatial data can be stored in regular files (e.g. shapefiles, GeoJSON), spatial databases offer a more efficient, scalable and powerful option, especially for large datasets. Spatial databases like PostGIS (an extension to PostgreSQL) and SpatiaLite have specialized data types, indexes and query operators to store, manage and analyze geospatial data.

Remote Sensing and Imagery
Remote sensing refers to acquiring information about the Earth‘s surface from a distance, primarily using satellite and aerial sensors. This includes capturing visible light as well as other wavelengths like infrared. Remote sensing imagery is a key source of geospatial data and is used for diverse applications like land use classification, change detection, vegetation mapping, etc.

Geospatial Analysis Workflow

A typical geospatial analysis project involves the following high-level steps:

1. Problem definition – Understand the business/research question and the desired outcomes
2. Data acquisition – Identify and gather the required geospatial and non-geospatial data from various sources
3. Data exploration – Examine the data to assess its quality, understand its characteristics and identify any issues that need to be addressed
4. Data preparation – Clean, preprocess and transform the data and get it in a suitable format for analysis
5. Analysis – Apply appropriate spatial analysis techniques, algorithms and models to extract insights from the data
6. Visualization – Create informative and visually appealing maps, charts and dashboards to present the results
7. Interpretation – Explain and translate the data insights into actionable recommendations to support decision making

GIS and Geospatial Software

Geospatial analysis is often associated with GIS (Geographic Information Systems) software that provide an integrated environment to visualize, manage and analyze geospatial data. Some popular commercial and open-source GIS software include:

• ArcGIS – A suite of GIS software products by Esri for creating and managing geospatial data, performing spatial analysis and creating maps.

• QGIS – An open-source cross-platform GIS application that provides data viewing, editing, and analysis capabilities.

• SAGA, GRASS GIS, GeoDa – Other specialized open-source GIS tools

For more programmatic workflows, geospatial analysis can be done using libraries in languages like Python and R. Some popular ones are:

• GeoPandas – A Python library that extends the Pandas data analysis library to support geospatial data.

• PySAL – A Python library for spatial analysis and statistics, focused on vector data.

• Rasterio – A Python library that makes it easy to read and write geospatial raster data.

• sf, raster, sp – R packages for handling vector and raster geospatial data.

We‘ll use Python and GeoPandas for the hands-on examples in the next section.

Geospatial Analysis with Python and GeoPandas

Let‘s see how we can use Python and the GeoPandas library to perform some common geospatial analysis tasks. We‘ll use a dataset of census tracts in California.

First, we need to import the required libraries and read the shapefile into a GeoPandas GeoDataFrame:

import geopandas as gpd
import matplotlib.pyplot as plt

ca_tracts = gpd.read_file("ca_census_tracts.shp") 
ca_tracts.head()

Visualizing the data

Let‘s create a simple plot to visualize the census tracts:

ca_tracts.plot(figsize=(10,10), edgecolor=‘gray‘, facecolor=‘none‘)
plt.autoscale(False)
plt.axis(‘off‘)
plt.show()

Reprojecting

The original data is in a geographic coordinate system (EPSG:4269). Let‘s reproject it to the California Albers projection (EPSG:3310) which is more suitable for area calculations:

ca_tracts_albers = ca_tracts.to_crs(‘EPSG:3310‘)

Spatial join

Suppose we have another GeoDataFrame ca_cities with polygons representing city boundaries in California. We want to find out which census tract each city belongs to. We can do a spatial join:

ca_cities_tracts = gpd.sjoin(ca_cities, ca_tracts_albers, op=‘within‘)

This does a point-in-polygon test to find the containing census tract for each city and adds the corresponding tract details to the ca_cities dataframe.

Calculating area

Let‘s calculate the area of each census tract in square kilometers:

ca_tracts_albers[‘area_sqkm‘] = ca_tracts_albers.area / 10**6

Choropleth map

We can create a choropleth map to visualize how a variable like population density varies spatially:

ca_tracts_albers[‘pop_density‘] = ca_tracts_albers.population / ca_tracts_albers.area_sqkm

fig, ax = plt.subplots(1, figsize=(10, 10))
ca_tracts_albers.plot(column=‘pop_density‘, cmap=‘Reds‘, edgecolor="none", ax=ax)
ax.axis(‘off‘)
plt.title("Population density by census tract in California")  
plt.show()

These are just a few examples of what‘s possible with GeoPandas. Refer to the GeoPandas documentation for more details and examples.

Advanced Topics

Some more advanced geospatial analysis topics worth exploring include:

• Spatial statistics – Techniques for quantitative analysis of spatial relationships, patterns and processes. E.g. spatial autocorrelation, hot spot analysis, spatial interpolation.

• Machine learning on geospatial data – Applying machine learning techniques to geospatial data for prediction, classification, clustering and anomaly detection.

• Big geospatial data – Processing and analyzing large volumes of geospatial data using distributed computing frameworks like Hadoop GIS, GeoSpark and Dask.

• Real-time geospatial analysis – Analyzing streaming geospatial data in near real-time for applications like vehicle fleet tracking, geo-targeted marketing, etc.

Learn More

To further your geospatial data analysis skills, here are some great resources:

• Learn GIS fundamentals with Esri‘s The ArcGIS Book
• An Introduction to Spatial Data Science in R
• GeoHackWeek – Annual geodata science workshop with coding tutorials in Python
• Kaggle‘s Geospatial Analysis micro-course
• The GIS StackExchange for Q&A on GIS concepts and techniques

Geospatial data analysis is a vast and exciting field with limitless opportunities to apply a spatial perspective to problem solving. This guide just scratched the surface and I hope it piqued your interest to dive deeper and add geospatial analysis to your data science toolkit!