Spacetime hotspot classification: Understanding collision patterns

Spatio-temporal analysis is crucial in extracting meaningful insights from data that possess both spatial and temporal components. By incorporating spatial information, such as geographic coordinates, with temporal data, such as timestamps, spatio-temporal analysis unveils dynamic behaviors and dependencies across various domains. This applies to different industries and use cases like car sharing and micromobility planning, urban planning, transportation optimization, and more.

In this example, we will perform a hotspot analysis to identify space-time clusters and classify them according to their behavior over time. We will use the location and time of accidents in London in 2021 and 2022, provided by Transport for London. This tutorial builds upon this previous one, where we explained how to use the spacetime Getis-Ord functionality to identify traffic accident hotspots.

Data

The source data we will use has two years of weekly aggregated data into an H3 grid, counting the number of collisions per cell. The data is available at cartobq.docs.spacetime_collisions_weekly_h3 and it can be explored in the map below.

Spacetime Getis-Ord

We start by performing a spacetime hotspot analysis to identify hot and cold spots over time and space. We can use the following call to the Analytics Toolbox to run the procedure:

carto-un

CALL `carto-un`.carto.GETIS_ORD_SPACETIME_H3_TABLE(
 'cartobq.docs.spacetime_collisions_weekly_h3',
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'h3',
 'week',
 'n_collisions',
 3,
 'WEEK',
 1,
 'gaussian',
 'gaussian'
);

carto-un-eu

CALL `carto-un-eu`.carto.GETIS_ORD_SPACETIME_H3_TABLE(
 'cartobq.docs.spacetime_collisions_weekly_h3',
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'h3',
 'week',
 'n_collisions',
 3,
 'WEEK',
 1,
 'gaussian',
 'gaussian'
);

manual

CALL carto.GETIS_ORD_SPACETIME_H3_TABLE(
 'cartobq.docs.spacetime_collisions_weekly_h3',
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'h3',
 'week',
 'n_collisions',
 3,
 'WEEK',
 1,
 'gaussian',
 'gaussian'
);

For further detail on the spacetime Getis-Ord check out the documentation and this tutorial.

By performing this analysis, we can check how different parts of the city become “hotter” or “colder” as time progresses.

Understanding hot and cold spots

Once we have identified hot and cold spots, we can classify them into a set of predefined categories so that the results are easier to digest. For more information about the categories considered and the specific criteria, please check the SQL reference in the documentation.

We can run the analysis by calling the SPACETIME_HOTSPOTS_CLASSIFICATION procedure using the previously obtained Getis-Ord results.

carto-un

CALL `carto-un`.carto.SPACETIME_HOTSPOTS_CLASSIFICATION(
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'cartobq.docs.spacetime_collisions_hotspot_classification',
 'index',
 'date',
 'gi',
 'p_value',
 '{"threshold": 0.05, "algorithm": "mmk"}'
);

carto-un-eu

CALL `carto-un-eu`.carto.SPACETIME_HOTSPOTS_CLASSIFICATION(
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'cartobq.docs.spacetime_collisions_hotspot_classification',
 'index',
 'date',
 'gi',
 'p_value',
 '{"threshold": 0.05, "algorithm": "mmk"}'
);

manual

CALL carto.SPACETIME_HOTSPOTS_CLASSIFICATION(
 'cartobq.docs.spacetime_collisions_weekly_h3_gi',
 'cartobq.docs.spacetime_collisions_hotspot_classification',
 'index',
 'date',
 'gi',
 'p_value',
 '{"threshold": 0.05, "algorithm": "mmk"}'
);

We can see how now we have different types of behaviors at a glance in a single map. There are several insights we can extract from this map:

• There is an amplifying hotspot in the city center that shows an upward trend in collisions.

• The surroundings of that amplifying hotspot are mostly occasional.

• The periphery of the city is mostly cold spots, but most of them are fluctuating or even declining.