Comparing and modeling land use organization in cities

The advent of geolocated ICT technologies opens the possibility of exploring how people use space in cities, bringing an important new tool for urban scientists and planners, especially for regions where data is scarce or not available. Here we apply a functional network approach to determine land use patterns from mobile phone records. The versatility of the method allows us to run a systematic comparison between Spanish cities of various sizes. The method detects four major land use types that correspond to different temporal patterns. The proportion of these types, their spatial organization and scaling show a strong similarity between all cities that breaks down at a very local scale, where land use mixing is specific to each urban area. Finally, we introduce a model inspired by Schelling's segregation, able to explain and reproduce these results with simple interaction rules between different land uses.Comment: 9 pages, 6 figures + Supplementary informatio

The city turned off: Urban dynamics during the COVID-19 pandemic based on mobile phone data

Due to the rapid expansion of the COVID-19 pandemic, many countries ordained lockdowns, establishing different restrictions on people’s mobility. Exploring to what extent these measures have been effective is critical in order to better respond to similar future scenarios. This article uses anonymous mobile phone data to study the impact of the Spanish lockdown on the daily dynamics of the Madrid metropolitan area (Spain). The analysis has been carried out for a reference week prior to the lockdown and during several weeks of the lockdown in which different restrictions were in place. During these weeks, population distribution is compared during the day and at night and presence profiles are obtained throughout the day for each type of land use. In addition, a spatial multiple regression analysis is carried out to determine the impact of the different land uses on the local population. The results in the reference week, pre-COVID-19, show how the population in activity areas increases in each time slot on a specific day and how in residential areas it decreases. However, during the lockdown, activity areas cease to attract population during the day and the residential areas therefore no longer show a decrease. Only basic essential commercial activities, or others that require the presence of workers (industrial or logistics) maintain some activity during lockdown

Probability density function of the weights considering all the links (points) and the missing links (triangles).

<p>(a) Barcelona and cell phone data. (b) Barcelona and Twitter data. (c) Madrid and cell phone data. (d) Madrid and Twitter data. In both cases <i>l</i> = 2 <i>km</i>.</p

Comparison between the non-zero flows obtained with the three datasets for the Barcelona's case study (the values have been normalized by the total number of commuters for both OD tables).

<p>Blue points are scatter plot for each pair of municipalities. The red line represents the <i>x</i> = <i>y</i> line. (a) Twitter and mobile phone. (b) Census and mobile phone. (c) Census and Twitter.</p

Box-plots of the Pearson correlation coefficients obtained for different hours between <i>T</i> and <i>P</i> (from the left to the right: the weekdays (aggregation from Monday to Thursday), Friday, Saturday and Sunday).

<p>The blue boxes represent Barcelona. The green boxes represent Madrid. (a) <i>l</i> = 2 <i>km</i>. (b) <i>l</i> = 1 <i>km</i>.</p

Temporal distribution patterns for the metropolitan area of Barcelona (<i>l</i> = 2 <i>km</i>).

<p>(a), (c) and (e) Mobile phone activity; (b), (d) and (f) Twitter activity; (a) and (b) Business cluster; (c) and (d) Residential/leisure cluster; (e) and (f) Nightlife cluster.</p

Map of the metropolitan area of Barcelona.

<p>The white area represents the metropolitan area, the dark grey zones correspond to territory surrounding the metropolitan area and the gray zones to the sea. (a) Voronoi cells around the BTSs. (b) Gird cells of size 2×2 <i>km</i>².</p

Comparison between the non-zero flows obtained with the Twitter dataset and the mobile phone dataset (the values have been normalized by the total number of commuters for both OD tables).

<p>The points are scatter plot for each pair of grid cells. The red line represents the <i>x</i> = <i>y</i> line. (a) Barcelona. (b) Madrid. In both cases <i>l</i> = 2 <i>km</i>.</p