For enquiries call:
+1-469-442-0620
HomeBlogData ScienceSpatial Analysis and Geospatial Data Science in Python
Spatial data is any form of data that helps us directly or indirectly reference a specific location or geographical area on the surface of the earth or elsewhere. Geographic Information systems, or GIS, is the most common method of processing and analyzing spatial data. This includes the entire stack of data management, manipulation, customization, visualization and analysis of the spatial data. GIS is a combination of programs working together, aiding users to understand and make sense of spatial data.
For example, if you were to work with GIS data for any project about spatial data within your geographical area, you would be dealing with different types of data such as vector data (lines - street data), polygons (boundaries of a geographic area) and point locations (buildings, skyscrapers, schools, etc.). These datasets would each exist as a layer of their own in GIS, where the placement of these layers becomes crucial for your understanding and analysis
The applications of GIS field and study extend much further than digital mapping and cartography, consisting of a multitude of categories such as remote sensing, spatial analysis, and geo-visualization. Here, in each of these applications, the spatial data becomes much more complex to use.
With this article, we shall tap into the understanding of spatial data and geospatial data analysis with Python through some examples and how to perform operations from spatial statistics Python libraries. We shall also go through a few basics and prerequisites that will be necessary for understanding spatial data, with how Python for spatial analysis has taken centre stage in today’s world for the application of GIS. Jupyter Notebook’s relevance is also included that allows us to work with two of the most popular software for GIS which is ArcGIS (Online cloud-based mapping and analysis solution) and QGIS (Quantum Geographic Information System, a free, open-source GIS software with many free online resources and maps available to download) for spatial data analysis with Python. This can be learned in the Data Science online courses.
Let us first try to understand what geospatial data is and look at a few examples. Geospatial data is information about describing objects, events, and other features with a location on or near the earth’s surface. The geospatial data combines the information about the location, which typically consists of the coordinates of the earth and also the attribute information, which talks about the characteristics, events, or phenomena regarding the objects, along with its temporal information, which is the life span or time at which the attributes and location exist. It typically consists of large datasets of spatial data obtained from multiple sources in different formats, including telephone data, satellite imagery, weather data, etc.
Much of the geospatial data that is available is open source (freely accessible to users) cause it consists of data that can reference roads, localities, water bodies, and public amenities, which are of general interest to a wide range of users and is helpful for a number of purposes to both public and private organizations. This open-source data is mainly made accessible through open standards, which are heavily supported within the geospatial community. This is due to the fact that primarily, a large number of agencies, both locally and globally, are involved in the generation of geospatial data, and secondarily because of the wide range of applications.
Geospatial analytics is used mainly to add timing and location to traditional data. Maps, graphs, statistics, and cartograms that depict recent and historical developments can be included in these visualizations. This added background enables a fuller understanding of the events. Easy-to-identify visual patterns and graphics are used to convey insights that might be missed in a large spreadsheet.
In the next section, we will be looking at performing geospatial data analysis in Python that employs the Python spatial analysis library
Now that we have understood what spatial/geospatial data looks like, we shall now look into a few exercises which will introduce us to how to use Python for geospatial data analysis. We shall start if with showing a few basic functions within the GeoPy library from Python, which uses third-party geocoders and other data sources to quickly find the coordinates of addresses, cities, nations, and landmarks all around the world. Each geolocation service that we use, such as Google Maps, Bing Maps, etc., has its class in geopy.geocoders which abstracts the service API.
Exercise 1: Let’s begin with checking if we can get the coordinates by entering the name of a popular place and vice versa. Here we shall use the Taj Mahal as a reference for our exercise. This example will serve as an introduction to working with coordinates and locations around the world.
Exercise 2: Here, we shall locate the Gateway of India on the map. For doing this, we shall be using the folium. Folium draws on the data manipulation and mapping prowess of the Python ecosystem and the leaflet.js package. The package makes it easier to visualize data that has been manipulated in Python on an interactive leaflet (Leading open-source JS library for mobile-friendly interactive maps) map. It enables both the binding of data to a map for choropleth visualizations and passing rich vector/raster/HTML visualizations as markers on the map.
In the next couple of exercises, we will look into using the GeoPandas library and how to perform a few operations using this library. GeoPandas is a Python library that expands the datatypes that pandas use to include geometric types for spatial operations. Shapely performs geometric operations. GeoPandas also uses matplotlib for charting and Fiona for file access.
Exercise 3: Here, we shall look into reading spatial data into the environment. Spatial data is stored as shapely data. As mentioned previously, GeoPandas makes use of Shapely’s geometric objects, which means the geometries are stored in a column called geometry (default column name), as shown below, which are shapely Polygon objects.
Once we have read the data into the environment using the read_file function from GeoPandas and performed a few transformations, we will go ahead and apply joins using a similar function as pandas.DataFrame.join() which is GeoPandas,DataFrame.join()
Exercise 4: In this next exercise, we shall see how we can calculate the area of the polygon that has been listed under the countries in Asia. Here by using the function of area for spatial data, we can have it calculated for us.
In the above section, we looked into spatial analysis in Python. Here we look into what makes Python the go-to language for spatial data and GIS. Python, in recent years, has seen widespread adoption across many domains. The rich and versatile libraries within Python make it well-suited for any sort of project one would want to pick up. This can be majorly attributed to two reasons:
GIScience (Geographic Information Science) has found a great receptive audience in Python due to the emphasis on readability, support across platforms, and lower start-up costs. Python offers flexibility through various modes of development for geospatial programming. Let’s look into the applications to understand how effective Python for geospatial analysis is.
We shall talk about the multiple spatial analysis Python libraries using a table to talk about a few of the popular or commonly encountered packages from each layer in the stack.
Layer | Package | Description |
Spatial Data Analysis | PySAL | To analyze clean spatial data in an interactive computational environment |
| GeoPandas | Pandas and shapely are combined to aid in working with geospatial vector data sets |
| GDAL/OGR | Allows working with both vector and raster data |
Spatial Modelling | spint | SPatial INTeraction Modeling package for a collection of tools for studying spatial interaction data |
| mesa | A Python framework for agent-based modeling |
| clusterpy | It is a library of spatially constrained clustering algorithms |
geovisualization | cartopy | A package designed for geospatial data processing in order to produce maps and other geospatial data analysis |
| folium | For creating visualizations on interactive leaflet maps |
| datashader | A data rasterization pipeline for automating the process of creating meaningful visuals for big data |
geoprocessing | shapely | A package for manipulation and analysis of planar geometric objects |
| rasterstats | A package for summarising raster datasets based on the geometrics of vector |
| pyproj | A package for performing cartographic transformations and geodetic computations |
Adding text to a map that only describes geographic features on a map improves the visualization of geographic information immensely. The main types of text defined are labels, annotation, and graphic tests.
The most common type of data loaded into a GIS software program is vector data. It represents geographic data as points, lines, or polygons.
The vector data is split into three types which are:
A raster, in its most basic form, is made up of a matrix of cells (or pixels) arranged into rows and columns (or a grid), each containing a value that represents some type of information. Raster includes digital aerial photos, satellite imagery, digital photos, and even scanned maps.
Data in raster formats represent real-world phenomena:
Without coordinate reference system (CRS) information that can be used by geospatial applications to display and manipulate the data correctly, a data structure cannot be considered geospatial. CRS information uses a mathematical model to link data to the earth’s surface. CRS then defines how the two-dimensional, projected map in your GIS relates to real places on the earth.
Components of CRS:
In cartography, one of the numerous techniques used to depict the three-dimensional surface of the globe or another spherical body on a two-dimensional plane is map projection (mapmaking). Usually, but not always, this process is a mathematical procedure (some methods are graphically based).
Georeferencing is defining the location of your raster data using map coordination and assigning the coordinate system of the map frame. Raster data can be viewed, queried, and analyzed with other geographic data using georeferencing.
There are generally four steps involved in Georeferencing process:
Finding geographic coordinates for place names, street addresses, and codes is a process known as geocoding (e.g., zip codes). Preprocessing and standardizing the format of the data you will be geocoding are often steps in the data cleansing process that come before geocoding. The resulting locations are output as geographic features with attributes that can be used for mapping or spatial analysis. There are many uses for geocoding, ranging from straightforward data analysis to customer and business management to distribution strategies. With geocoded addresses, you can visualize the locations of the addresses and spot patterns in the data.
In this section, we will go over the two most powerful libraries from Python. When it comes to something like Geospatial analysis, it is important to use the right packages, and in Python, they are shapely and GeoPandas, which is also taught in Bootcamp Data Science.
Follow along; the shapely objects are as follows; we have polygons, lines, and points. One of the features that helps shapely work at scales is that we can use multiple objects as part of the same object. In addition, we also have elements such as multipolygons, multiline, and multi-points.
Now, a question arises, where is this feature useful? It is utilized when we define objects that have multiple geometries, such as countries that may have islands and other such physical landforms.
Let’s quickly look over some of the code that we can use to make some of the plots. First, we start off by importing the required packages to be able to plot the different geometries. Here, we import shapely, from where we import the point, linestring, polygon, multi-point, and multi-polygon components.
Next, we plot a point to see what it looks like.
Post this; we proceed to look at the distance between two points, where the default distance measuring algorithm used is the Euclidian distance.
Next, we plot multiple points.
Now that we have understood well how points are plotted, we proceed to plot a linestring based on the points that we select.
Post this, we would like to also analyze the distance of the line that has been plotted, and we are also able to get the bounds of the lines that have been plotted. This essentially shows us the boundaries of the plotted points.
Now that we have understood the bounds of the points, we can proceed and plot a full-fledged polygon. In this case, we will plot the little arrows that we generally see in Google Maps.
In the next section, we will look at how we can load the data.
First, we will need to install the packages that are required to be able to leverage GeoPands. Depending on your operating system, you can install the geopandas library. In this case, we have directly run this code on Google Colab. In fact, if you follow along with the code, you can do the same on Google Colab.
Now, we go ahead and import the relevant libraries to perform geospatial analysis.
In the next steps, we will read the data from a region called ‘naturalearth_lowres’, which contains a low-resolution image of the geometry of all the counties, along with some additional parameters such as GDP (Gross Domestic Population) and Population metrics.
Next, we will import the relevant dataset from GDP so that we can effectively read the data.
In the next section, we will leverage a coordinate reference system or CRS, as it is popularly known to obtain more information about the dataset.
In this code snippet, we will map the population density of the world map based on the GDP data.
If we note carefully, we can see the various components of a Coordinate Reference System (CRS):
Finally, in this section, we will see the figure size based on your liking and plot the population based on the density of the population, where light green represents the most highly dense regions, and dark purple denotes the lowest population density.
Jupyter notebook is a powerful Python tool that allows users to create and share documents containing codes, visualizations, explanatory texts, and equations. The few main reasons that we can attribute to the growing popularity of Jupyter Notebook could be as follows:
Today, Jupyter notebooks have become the go-to tool for GIS analysts who choose to do spatial analysis with Python for a multitude of tasks such as spatial data manipulation, spatial analysis, visualization, etc. Considering all the challenges that were a part of GIS software for doing geospatial analysis, which includes
The GIS community quickly realized its potential and adopted Python as a tool for GIS analysis; however, Jupyter notebook provided the missing piece of becoming an easy-to-use tool that replaced the code editor as a working environment. Many geospatial Python packages are already available, including everything from geospatial data management to mapping capabilities inside a Jupyter Notebook.
To start utilizing the Jupyter Notebook application within a desktop GIS, the ArcGIS Notebook inside ArcGIS Pro comes with a default installation. QGIS users will need to install the IPython QGIS Console plugin. This gives access to the IPython Console inside of QGIS. The IPython Console allows users to execute commands and interact with data inside IPython interpreters, which enables spatial data science Python analysis, which can also be learnt in Data Science using R syllabus.
In this article, we have covered different aspects of Geospatial analysis. We started by understanding what geospatial data is, which typically gives information about objects, events, and other features with a location on or near the earth’s surface. Now, with this data, we also looked into how we can get started with working on it using different libraries such as GeoPy and GeoPandas. The base idea was to understand what spatial data looks like and how we can perform simple analysis using Python spatial analysis libraries. The adoption of Python shows how Python was accepted by many GIScientists as a go-to source for building desktop applications and standalone geospatial applications.
The Python ecosystem consists of numerous libraries that can be utilized for tasks across the spectrum to work with geospatial data. We looked into the basic concepts and terminologies of spatial data, which include text, vector, and raster forms of data, what a Coordinate reference system is and how it is useful for Map projections, georeferencing, and geocoding. Also, we went through to understand the pain points of the GIS and how Jupyter Notebook emerged as one of the leading options for having a single working environment for working with spatial analysis using Python.
The fact that Python is easy to learn as a language and the availability of numerous geospatial data analysis Python libraries makes it very adaptable to users for Geospatial analysis. Major GIS platforms like ArcGIS and QGIS have adopted Python as the principal scripting, toolmaking and analytical language. Python, according to experts, is also simpler to use than other high-level languages since it allows for a wide range of coding paradigms, including imperative, functional, procedural, and object-oriented ones.
R in comparison to Python, conducting spatial analysis with R can be just as simple. R programming language has seen an increase in the number of packages that are contributed towards GIS. R is also ideal for swiftly developing and visualizing vectorised data, which makes it simpler for people who don’t want to put extra time into reading through documentation or creating hundreds of lines of code to accomplish something simple.
It is crucial to realise that both of these programming languages for spatial analysis accomplish different things. Python is excellent for automation when performing tasks like network analysis or cost surface analysis for batches of data. But when working with large datasets, such as when conducting multiple regression analysis, R is considered indispensable. Hence it becomes tricky to choose between both of them and wiser to use them for their own use cases.
The availability of a great number of libraries from spatial analysis Python packages makes Python a very powerful tool for spatial data analysis. It is simple as a process to load spatial data into Python using libraries such as GeoPandas and perform spatial statistical analysis on the same dataset. We have various other libraries which enable the user to create visualizations suitable to any use case at their disposal.
This is an interesting question. The five primary components that we need to take care of when dealing with spatial analysis are methods, people, data, software and hardware. Each component is critical to the overall analysis as it feeds into the overall result of the spatial analysis.
The spatial analysis method consists of the following components:
There are several uses for spatial analytic technology in both the public and business sectors. In addition to addressing global issues, it can be used to enhance local environments or communities. Time-lapsed spatial data analysis can be used to spot patterns and trends as well as anticipate future events quite accurately.
Spatial data analytics uses AI and ML applications to process enormous volumes of data with high levels of precision and efficiency.
| Name | Date | Fee | Know more |
|---|
