Safety of commute to schools in Moscow
Project year
2019
Team
City's Backyard
Project role
Data science & viz
Made for
Non-commercial organization for urban design / municipal deputy for Presnensky district of Moscow
Challenge
Alexey Radchenko from non-commercial organization for urban design contacted me with a task to make an analysis for all schools in Moscow, looking at how many car accidents with pedestrians took place in isochrones around each school during 2015 to 2018.
After that, a municipal deputy for Presnensky district of Moscow asked me to do an in-depth analysis of her district for the same matter of street safety for school kids.
After that, a municipal deputy for Presnensky district of Moscow asked me to do an in-depth analysis of her district for the same matter of street safety for school kids.
Interactive visualization of street safety for Moscow schools
The map was created with Carto Builder, data is sourced from an open dataset of Moscow schools and car accidents data was loaded via scraping scripts from open car accidents website. Legend on the left shows number of people hit by cars in a 5-minute walking isochrone around schools during research years (2015-2018).
You can filter by number of pedestrians hit by cars on the right. The pop-up show info about the school.
You can filter by number of pedestrians hit by cars on the right. The pop-up show info about the school.
The map shows Moscow schools and 5-minute walking isochrones, coloured by number of pedestrians hit by cars during 2015-2018, thus understanding the safety of immediate surroundings of each school.
Workflow
There are a lot of talks in Moscow (and Russia overall) about safety in schools, but most of the time it is circling around more and more security cameras, security staff, fences around schools that «guard kids from the outside world». But we thought that on the contrary, we should make the outside world - meaning neighbourhood, district, the city - more safe for everyone, also making it safer for our kids. So in this exploration we've established an idea that says: the school is more commute-safe if the immediate surroundings (5-min walkshed / isochrone) has the smallest counts of pedestrians hit in the previous 4 years.
I've done the exploration in Python: created isochrones for all 638 main buildings of the schools (that is what open dataset offered in coordinates), downloaded all car accidents for 2015-2018 with a scraping script from an open data community, filtered accidents to only include pedestrian - related, then counted all accidents in each 5-minute walking isochrone. The visualization I've done via Carto can be seen above.
After some time, a similar task came to me from a municipal deputy for Presnensky ditrict of Moscow. This time I've used the schools dataset downloaded from OpenStreetMap, after that we've added some schools manually with the help from client.
For this map I've also included a filter for counting accidents in each of 4 years, added points of accidents with information on each of them. The map for safety of schools in Presnensky district can be seen below, also done in Carto.
I've done the exploration in Python: created isochrones for all 638 main buildings of the schools (that is what open dataset offered in coordinates), downloaded all car accidents for 2015-2018 with a scraping script from an open data community, filtered accidents to only include pedestrian - related, then counted all accidents in each 5-minute walking isochrone. The visualization I've done via Carto can be seen above.
After some time, a similar task came to me from a municipal deputy for Presnensky ditrict of Moscow. This time I've used the schools dataset downloaded from OpenStreetMap, after that we've added some schools manually with the help from client.
For this map I've also included a filter for counting accidents in each of 4 years, added points of accidents with information on each of them. The map for safety of schools in Presnensky district can be seen below, also done in Carto.
The map shows Presensky district schools and 5-minute walking isochrones around them, coloured by number of pedestrians hit by cars during 2015-2018. We can filter the data by each year / number of accidents.
White points represent schools, green points - accidents involving pedestrians.
White points represent schools, green points - accidents involving pedestrians.
Outcome
First part of the project was made for a post by Alexei Radchenko (urban blogger and non-commercial urbean design consultant) that sparked a conversation about the real safety of schools.
Second part was used by municipal deputy of Presnensky district for a municapal and city motion on programs for street safety, new pedestrian streets, street design safety, cycling lanes and more. To be honest, which better outcome of a project can there be?
Second part was used by municipal deputy of Presnensky district for a municapal and city motion on programs for street safety, new pedestrian streets, street design safety, cycling lanes and more. To be honest, which better outcome of a project can there be?
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