Visualizing geospatial data can help users communicate how different variables correlate to geographical locations by layering the same variables over maps.
Harry Kane, a player for English soccer outfit Tottenham Hotspur and one of the world’s most prolific strikers, was facing questions over his form before a match against Premier League side Newcastle United on July 15, 2020.
Pundits were questioning whether he was struggling to replicate his scintillating form of previous seasons, with some attributing that to the pragmatic tactics of the now-former Tottenham coach Jose Mourinho.
Sky Sports Pundit Paul Merson pointed at the 36 touches that Kane had in a 1-1 draw against Premier League side Manchester United on January 1, 2020, as proof that Kane was drifting away from a central striker’s natural positions as Tottenham were sitting too deep in their half prompting him to do more work in deeper areas. This, Merson said, was limiting Kane’s ability to impact games.
Kane scored two goals versus Newcastle and allayed fears over his form, but the big picture still needed to be looked at.
Was Kane struggling in a Mourinho team?
Sky Sports used a heat map comparison to assess Kane’s impact in the 2019/2020 season versus the 2017/2018 season to assess whether this was true. In the former season, Kane was playing under Mourinho, while in the latter, he was playing under Pochettino.
The heat maps showed that under the more progressive Pochettino, Kane touched the ball more in opposition areas, making him more of a threat than his field positions under Mourinho, renowned for his safety-first methods.
You’d be wondering at this point why I sound like a sports commentator to start an article about visualizing geospatial data. Well, there is a very good reason.
Heat maps are used heavily in sports in the current age to illustrate the contribution of individual players or a team’s performance. Heat maps show defensive and attacking contributions, distance covered during a match, and touches in the whole or particular pitch area.
Heat maps are not just used in sports, they are used in different professions including research, medicine, engineering and business analysis.
This brings us to the topic of the day: how Geospatial Data is used for visualization.
What is Geospatial Visualization?
As it stands, data is at the heart of many organisations’ operations. So, visualizing data has become very important in conveying a message using geographical elements.
Creating abstract shapes and using colours has become a key way to track geographic patterns and tell stories about human interactions. Maps are visualized using colours to contrast geographic patterns.
There are seven techniques for visualizing geospatial data.
1. Chloropleth Maps
Chloropleth maps use different shading patterns and colours for different regions. Every shading pattern and colour corresponds to a different set of values that a variable can accommodate.
Chloropleth maps are designed to be ideal for visualizing geographic clusters but could be imprecise in the event the size of a geographical region eclipses the colour.
2. Heat Maps
As captured in the Harry Kane illustration at the beginning of this piece, heat maps are being used in various fields including sports.
Heat maps become instrumental when depicting large sets of continuous data on a map using a colour spectrum. The deeper the colour, the higher the activity taking place in the marked region. A heat map differs from a Chloropleth map in that the colours in a heat map do not tally with geographical boundaries.
Heat maps are especially useful for showing the places where the most activity takes place or to identify patterns. However, heat maps are applied with caution to avoid distorting data accuracy.
3. Hexagonal Binning
Hexagonal binning is a data visualization format that uses regular hexagons to create a grid in a map. With the grid, the map can be shaded or coloured like a chloropleth map.
Hexagon grids work best if there are a lot of granular points at play and when there are reservations about adulterating quality by using data extrapolation methods.
The hexagon is preferred because of its marginal resemblance to a circle but unlike in a circle, it is far much easier to form continuous grids with a hexagon.
However, one downside is that it becomes a challenge to zoom in and out of the visualization by dividing hexagons or aggregating them.
4. Dot Map
A dot map, also referred to as a dot density map or dot distribution map, uses a dot to represent a variable.
Dot maps can also be identified as scatterplots on a map and can be useful for showing spatial patterns.
A dot map denotes an accurate representation of the variable’s value in granular locations on the map. Geocoding the data accurately during the data-collection stage is instrumental for guaranteeing location accuracy.
5. Cluster Map
Cluster maps help in representing large tracts of data using a single point. Every cluster is either labelled with the number of points that have been piled together or is relatively sized.
Clusters work best in interactive maps where the user can trickle down to see individual data points contained in a cluster. Cluster maps make it easier to avoid overcrowding a map when there are many overlapping data points in a small geographical region.
6. Bubble Map
Bubble maps are instrumental in representing two variables— one by varying the size of the colour and the other by varying the size of the bubble — simultaneously in one single visualization.
Bubble maps are also important as they help users digest a sense of three parameters at the same time through the colour, size and location of the bubbles.
7. Cartogram Map
When it comes to a cartogram map, the mapping variable is shown in a diagrammatic form. On multiple occasions, the mapping variable replaces the land area or distance in the map consequently, the map gets distorted in proportion to the mapping variable.
A cartogram is considered the best geospatial data representation technique as it represents the mapping variable in relation to the corresponding geographical area.
Visualizing Geospatial Data For Public Transport
There are a number of ways to visualize Geospatial Data in public transport. Let’s look at a study published in the International Journal of Geo-Information on June 3, 2016, authored by Martin Loidl, Gudrun Wallentin, Ryta Cyganski, Anita Graser, Johannes Scholz and Eva Haslauer dubbed GIS and Transport Modelling — Strengthening the Spatial Perspective.
Development of Modern Data Models
According to the authors, most GIS-T applications and transport models either rely on object-oriented or relational databases. In this paper, they underscore that the requirements for data models vary in different environments. Hence, they argue that visualizing geospatial data helps design models be flexible and handle large tracts of data from different sources with different resolutions and formats.
According to the authors, lessons drawn from visualizing geospatial data can be incorporated in designing public transport facilities and higher-order street networks. The six authors aver that the cadre of spatial aggregation into travel analysis zones is high and has proven effective for this purpose.
Cost & Time Efficiency
Visualizing geospatial data makes it easy for users to interpret the information and tailor the information to suit their needs. This is enabled by the fact that the different geospatial data visualization techniques are designed to break down complex information in an easy to read and in a visually appealing manner.
This saves on time and resources enabling users in different fields to achieve results more efficiently. For instance, as captured in the dot map definition above that shows the number of the Hispanic population in the United States, transport authorities, after establishing the demographics and psychographics of a particular region, can divert resources to fit the needs of the residents of that particular area allowing resources and time to be spent more prudently.
In sports, geospatial data visualization techniques like heat maps are used to analyze performance as well as help limit the potency of the opposition. In business, they are used to monitor trends, psychographics and to measure risk. In medicine, they are used to provide comprehensive information to enable medical practitioners to make informed decisions for the good of their patients.
The bottom line is that better results and efficiency are achieved when geospatial data techniques are used.
In the current age, that is the difference between those applying these techniques and those who are not.