Exploring the relationship between income inequality and urban mobility in Madrid, Spain
- 4 minsMobility and Income Segregation in Madrid, Spain
This thesis project explores the relationship between income inequality and urban mobility in the city of Madrid. Literature shows that bringing mobility into the study of inequality and segregation can bring an extra dimension to its study. By using a granular mobility dataset of trips throughout a month of 2022 (obtained from MITMA Estudios Básicos), a series of methods are developed to measure which districts play a central role in pulling and pushing travellers, and to measure which income groups travel more, and longer distances within the city. This research provides insights on the ongoing income segregation patterns in Madrid, and identifies slightly homophilic travelling tendencies. It also reveals that lower income groups tend to travel more and longer on average than higher income groups. This research contributes to the field of urban studies and emerging fields like Science of Cities or Urban Data Science, and aims to aid policymakers to identify mobility isolated areas in the urban space to develop effective policies to reduce the negative effects of gentrification and segregation.
The main research question driving my research is:
To what extent do residents from economically segregated districts in Madrid move differently than those in other districts?
To answer this question, I developed the following hypotheses to guide research:
Low-income and highly segregated districts push residents out for daily mobility purposes, like work or recreational, whereas high-income and low segregated districts pull residents, as these districts tend to offer higher amenities and work possibilities.
Residents from low-income and highly segregated districts tend to make a higher number of trips to high-income and low segregated districts than vice versa.
Residents from low-income and highly segregated districts tend to perform, on average, longer distance trips than those living in high-income and low segregated districts.
You can read the paper by clicking the here: Paper
You can view the resulting mobility network by clicking here: View Network of Madrid
Methods and Results
Exploratory Analysis
Method
I perform an exploratory analysis to address the state of income distribution in Madrid during 2021. This allows me to select the appropriate income variables for the study. To do so, (1) I plot and study the distribution of six income estimators to understand which are more accurate for representing the income and segregation levels of the districts. Based on this analysis, I select two key variables to understand the interactions between mobility and income: the median income per consumption unit and the Gini index by district. (2) I then calculate Global and Local Moran indices for each district based on the median income per consumption unit to illustrate the clustered patterns of income distribution in the city.
Results
Based on this analysis, I conclude that there is spatial autocorrelation in terms of the median income per consumption unit in the city of Madrid in 2021, with significant clustering patterns in 48\% of the districts.
Test Hypothesis 1
Method
I build an origin-destination (OD) dataset containing normalised trip counts within and between districts. These counts are used to build a mobility network and a consequent adjacency matrix. The construction of a network allows to quantitatively analyse the in and out-weights of the nodes. Lastly, I build complementary assortativity matrices based on trips between income and Gini index deciles to study whether there are any mobility patterns across deciles.
Test Hypothesis 2
Method
I use the previously created trip counts dataset. The districts of origin and destination are classified into four quantiles based on the median income and also on the Gini index values, and I compare the differences among the furthest quantiles. This allows to further analyse the total number of trips between and within districts based the income background of the individuals.
Results
I conclude that residents from lower median income per consumption unit districts perform 1.33% more trips to higher income quantiles than vice versa. I also conclude that residents from low segregated districts also tend to perform around 1.85% more trips to higher segregated districts than vice versa. The differences in intra-quantile share of trips are 6.57% and 2.3% for the median income and Gini index respectively.
Test Hypothesis 3
Method
I analyse the average distance of the trips based on the four aforementioned quantiles by variable. I retrieve the average trip distance by OD pair, and compare these based on the top and lowest quantile.
Results
I conclude that residents from the lowest median income quantile travel slightly longer distances on average than residents from the highest median income quantile. Residents from the highest Gini index quantile travel shorter distances on average than residents from the lowest Gini index quantile. I also conclude that the average distance of most trips falls under a 0.5 km - 2.5 km window, and that those performing the longest average trips belong to the low-income and low-Gini quantiles.