Analyzing and optimizing cities cycling facilities

CARTO for embedded analytics

The beauty of CARTO is that we meet the users where they are in their spatial analysis journey. From advanced spatial capabilities to simple mapping tools to visualize, analyze & share in a matter of minutes. To accelerate your spatial app development efforts, you can use CARTO embedded capabilities inside low-code tools like this one created in Slides.com. In this story map, we will analyze spatial data from Paris municipality (hosted in Google BigQuery) to understand the territorial context of the cycle lane network, and where are the areas with higher potential to improve the cycling connection.

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MAP 1 OF 3 

Cycling network distribution in Paris

While bicycle travel has exploded in Paris during the COVID-19 health crisis (+ 67% in 2020), bicycle users are demanding a more ambitious bicycle policy, starting with a complete and uninterrupted cycle network.

This map shows the Paris municipality cycling network, including different typologies of roads, streets, cycleways, or paths that can be used for cycling.

The longest cycling networks are residential roads and dedicated cycleways. The 12éme and 15éme arrondissement are the areas with the longest cycling network.

If we start playing with the map widgets and filter the cycle network on the categories of cycleways and pedestrian streets (which are the safest), We can see that there is a lack of cycling facilities and connections in certain areas of Paris, for example, the southern areas between the Latin Quarter and Montparnasse (5éme, 6éme) or the north-western areas (16éme, 17éme, and 18éme arrondissement).

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MAP 1  OF 4 

Cycling network distribution in Paris

While bicycle travel has exploded in Paris during the COVID-19 health crisis (+ 67% in 2020), bicycle users are demanding a more ambitious bicycle policy, starting with a complete and uninterrupted cycle network.

This map shows the Paris municipality cycling network, including different typologies of roads, streets, cycleways, or paths that can be used for cycling.

 

The longest cycling networks are residential roads and dedicated cycleways.

 

The 12éme and 15éme arrondissement are the Communes with the longest cycling network.

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Photo by Eddie Junior on Unsplash

Explore the interactive CARTO map

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About this story map

 

CARTO embedded capabilities

 

The beauty of CARTO is that we meet the users where they are in their spatial analysis journey. From advanced spatial capabilities to simple mapping tools to visualize, analyze & share in a matter of minutes. To accelerate your spatial app development efforts, you can use CARTO embedded capabilities inside low-code tools like this one created in Slides.com. In this story map, we will analyze spatial data from Paris municipality (hosted in Google BigQuery) to understand the territorial context of the cycle lane network, and where are the areas with higher potential to improve the cycling connection.

With data stored in                    Connected by                            Displayed on top of

 

 

 

 

 





 

Data sources 

All the data used for this visualization are available in BigQuery. The original sources for the data are:​

    - Cycling facilities in Île-de-France
    - Points of Interest - OSM - CARTO Data Observatory
    - Population by Communes Île-de-France

    - Paris municipality geography

HOW HAS THIS BEEN MADE
CARTO offers a simple way to add Tables or direct SQL from BigQuery
into Google Maps. Once your maps are created, it is really simple to
share them across your organization, make them public or embed them
in low-code tools to create quick spatial applications.


 

MAP 2 OF 3 

Analyzing the potential of attraction of areas uncovered by cycleways

Urban planners rely on a wide range of criteria to decide the expansion of cycling networks, and almost all of them have a location component. One of the key ones is the potential to attract mobility. For example, since home-work trips cause a great deal of traffic congestion and pollution, it is really important to properly connect populated residential neighborhoods with areas with a high concentration of businesses and offices.

The following dual map shows a comparison of the population living in Paris neighborhoods (on the left) and areas with higher offices concentrations (on the right). In both, the cycling network overlaps. In the previous analysis, we saw how there was a lack of cycling networks in the north-western neighborhoods (16éme, 17éme, and 18éme arrondissement). Now we can verify that those are areas with a high population, that will potentially move to areas with a higher number of offices, mostly located in the center, the south of Paris and the business district of La Défense.

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Photo by Stephan K on Unsplash

Explore the interactive CARTO map

   Next

   Next

   Back   

MAP 2 OF 4 

Analyzing the potential of attraction of areas uncovered by cycleways

Urban planners rely on a wide range of criteria to decide the expansion of cycling networks, and almost all of them have a location component. One of the key ones is the potential to attract mobility. For example, since home-work trips cause a great deal of traffic congestion and pollution, it is really important to properly connect populated residential neighborhoods with areas with a high concentration of businesses and offices.

The following dual map shows a comparison of the population living in Paris neighborhoods (on the left) and areas with higher offices concentrations (on the right). In both, the cycling network overlaps. In the previous analysis, we saw how there was a lack of cycling networks in the eastern neighborhoods (16éme, 17éme, and 18éme arrondissement). Now we can verify that those are areas with a high number population that will potentially move to areas with a higher number of offices, mostly located in the center, the south of Paris and the business district of La Défense.

   Next

back_arrow

Photo by Stephan K on Unsplash

Explore the interactive CARTO map

   Next

   Next

   Explore

About this story map

 

CARTO embedded capabilities

 

The beauty of CARTO is that we meet the users where they are in their spatial analysis journey. From advanced spatial capabilities to simple mapping tools to visualize, analyze & share in a matter of minutes. To accelerate your spatial app development efforts, you can use CARTO embedded capabilities inside low-code tools like this one created in Slides.com. In this story map, we will analyze spatial data from Paris municipality (hosted in Google BigQuery) to understand the territorial context of the cycle lane network, and where are the areas with higher potential to improve the cycling connection.

With data stored in                    Connected by                            Displayed on top of

 

 

 

 

 





 

Data sources 

All the data used for this visualization are available in BigQuery. The original sources for the data are:​

    - Cycling facilities in Île-de-France
    - Points of Interest - OSM - CARTO Data Observatory
    - Population by Communes Île-de-France

    - Paris municipality geography

HOW HAS THIS BEEN MADE
CARTO offers a simple way to add Tables or direct SQL from BigQuery
into Google Maps. Once your maps are created, it is really simple to
share them across your organization, make them public or embed them
in low-code tools to create quick spatial applications.


 

MAP 3 OF 3 

Analyzing Points of Interest in areas uncovered by cycleways

In addition to the population and businesses offices concentration, other attraction points can be interesting to analyze the potential of new cycling networks.

In this map we can analyze where are the highest concentrations of Shops and Tourism Points of Interest (hotels, tourist places, museums, etc). This will allow us to analyze other potential places that will attract mobility, and that is uncovered by the current cycle lane network.

In this case, we can also verify the analysis made in the first map, and see how it could be improved the connections in the southern areas of the center of Paris, between the Latin Quarter and Montparnasse (5éme, 6éme).

back_arrow

Photo by Finn on Unsplash

Explore the interactive CARTO map

   Back   

MAP 2 OF 4 

Analyzing Points of Interest in areas uncovered by cycleways

In addition to the population or offices concentration, there are many other points of attraction that can be interesting to analyze the potential of the new cycle network.

In this map it can be analyzed where are the highest concentrations of Tourism Points of Interest (hotels, touristic places, museums, etc), and where are more density of Shops. This will allow us to analyze other potential places to attract mobility that are uncovered by the current cycle lane network.

In this case, we can see how it could be improved the connections in the southern areas of the center of Paris between Latin Quarter and Montparnasse (5éme, 6éme).

back_arrow

Photo by Finn on Unsplash

Explore the interactive CARTO map

About this story map

 

CARTO embedded capabilities

 

The beauty of CARTO is that we meet the users where they are in their spatial analysis journey. From advanced spatial capabilities to simple mapping tools to visualize, analyze & share in a matter of minutes. To accelerate your spatial app development efforts, you can use CARTO embedded capabilities inside low-code tools like this one created in Slides.com. In this story map, we will analyze spatial data from Paris municipality (hosted in Google BigQuery) to understand the territorial context of the cycle lane network, and where are the areas with higher potential to improve the cycling connection.

With data stored in                    Connected by                            Displayed on top of

 

 

 

 

 





 

Data sources 

All the data used for this visualization are available in BigQuery. The original sources for the data are:​

    - Cycling facilities in Île-de-France
    - Points of Interest - OSM - CARTO Data Observatory
    - Population by Communes Île-de-France

    - Paris municipality geography

HOW HAS THIS BEEN MADE
CARTO offers a simple way to add Tables or direct SQL from BigQuery
into Google Maps. Once your maps are created, it is really simple to
share them across your organization, make them public or embed them
in low-code tools to create quick spatial applications.