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How much “15-minutes” is your city? Using open data to measure walking proximity

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How much “15-minutes” is your city? Using open data to measure walking proximity
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351
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CC Attribution 3.0 Unported:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
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Production Year2022

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Abstract
The challenges posed to the current urban mobility model by pollution-related and urbanisation issues have resulted in significantly increasing the importance of urban resilience. Mobility management, pandemics’ spreading, equal access to services and climate crisis are just some of the crucial issues that falls within the definition of urban resilience. One very promising solution aiming to solve many of these issues has been presented in 2016 by Professor Carlos Moreno under the name of “15-minutes city”. The paradigm is based on the idea that every citizen should be able to reach the essential services (supermarkets, shops, parks, etc) walking not more than 15 minutes from their home. The model is being tested in some metropolitan cities around the world (e.g. Paris). However, reorganizing the city so that it presents a 15-minutes structure is not an easy task. It requires large resources and a careful planning based on data, to make sure that the project undertaken will actually have a positive effect on the urban mobility and no asset is wasted on useless projects. The Business Innovation team of Dedagroup Public Services used Open Street Map data to develop an index to detect the local level of proximity within the city, showing both the areas that already conform to the 15 minutes model and the ones that do not, where taking action would improve the quality of life of the citizens living there. The presentation will be focused on this proximity index, describing the assumptions behind its definition, such as the choice of city services to be considered essential, the nature of the road network used to compute walking distances and the area tiling chosen for the task. The index will be then showcased on the city of Florence, together with an analysis of the city from a proximity point of view and a what if scenario: how would the index change if the municipality (and other relevant stakeholders) decided to make interventions on low proximity areas? The case of Ferrara will be also presented to show that the proximity index can be the basis for further analyses: coupling the index with resident population count can help to spot the areas that are both under-served and highly populated, that are the ones where more people would benefit from improvements.
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Transcript: English(auto-generated)
Can you hear me? Yes. I'm Pier Giorgio from DataNext, Italy, and I'm presenting on behalf of my colleague, Beatriz. And this presentation is about 15-minute city. If you are not aware of the 15-minute city concept, it is nothing new, but became quite popular in the last years,
thanks to this guy, the professor Carlos Moreno, and also thanks to the mayor of Paris. Basically, a 15-minute city as a concept is a residential urban area in which most of the daily needs of residents can be met by walking or by cycling.
So, Beatriz started to ask about how much 15 minutes are our cities, and we started to think of Italian cities.
At the beginning of this year, she proposed to work on a proximity index in order to calculate at the polygon level the value of a sort of 15-minute-ness index. And she divided the entire Italy into a grid of more than 3 million hexagons, 250 meters side.
And then she assigned a value of the index, and the value is corresponding to the average time to reach daily points of interest on foot or by bike.
And which data are the input for the Arrigoli Implant. Basically, open street map data in particular points of interest that are listed here are amenities like food shops, restaurants, education, schools, banks and public offices and so on.
And road network. Of course, from the road network, she excluded the high-speed roads where pedestrians or bikers are not supposed to be. And we started from the Emilia-Romagna region, and then we launched the algorithm at the entire Italian level, this is Turin.
And this picture shows the average index, that is, the average value considering all categories. But we can, of course, select a specific category like, for instance, education in order to better understand
the 15-minute-ness regarding that specific subset of points of interest or entertainment like cinemas or theatres. Of course, the algorithm and the index itself is not thought to be a sort of mouth of the truth, that is, a marble mask we have in Rome.
Just like other indexes that have been already implemented by others cannot be considered as mouth of truth. To get closer to the truth, we should consider other data that are not available in open street map or are not fully available in open data portals.
And we started to work with the municipality of Ferrara, and the GIS department provided us aggregated data about population distribution.
The aggregation is based on the same hexagonal grid we used for the index. And by comparing the distribution of population with the index, we derived this second index.
We called it the score for the index, showing the areas where decision-makers by both public sector and private sector are supposed to take decision in order to improve the 15-minute-ness, so the quality of life of their city.
That's it. I thank you very much. If you have got any questions.