Sidewalk networks: Review and outlook, by D. Rhoads, C. Rames, A. Solé-Ribalta, M.C. González, M. Szell & J. Borge-Holthoefer, published in Computers, Environment and Urban Systems

From a transport perspective, increasing active travel –and walking in particular– is crucial for the future of sustainable cities, as reflected in global decarbonisation policies and agendas. Further, walking is much more than a mere mode of transport: it provides a fundamental social function, fostering vibrant cohesive communities. Arguably, walking and its associated infrastructure –sidewalks– should rank among the highest priorities for planning authorities. However, efficiency- and speed-driven urbanisation has gradually reallocated street space to private cars, leading to automobiles being the prioritised mode of transport today. Empirical research has generally followed suit, and a systemic understanding of walking as a phenomenon is largely missing, i.e., questions like how connected, resilient, accessible, or socially equitable is the pedestrian infrastructure of whole neighbourhoods and cities. Such relative neglect of sidewalk network research is, first and foremost, the consequence of a generalised lack of publicly available data on sidewalk infrastructure worldwide. A second reason might be its apparent lack of interest from a systemic standpoint: pedestrian mobility does not produce coordination challenges on the scale that cars do. In this work, we confront this perception by showing that there is ample research potential in the study of system-wide sidewalk networks, with both structural and dynamical challenges which might be critical to pursue the latest aspirations towards sustainable mobility in cities.

Future directions in human mobility science, by L. Pappalardo, E. Manley, V. Sekara, L. Alessandretti, published in Nature Computational Science

We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behavior and provide useful tools for modelers. Finally, we discuss how progress on these research directions may help us address some of the challenges our society faces today.

We welcome the summer with 3 new diverse papers!

1. BikeDNA: A tool for bicycle infrastructure data and network assessment, by A. Rahbek Vierø, A. Vybornova & M. Szell, published in Environment and Planning B
   
   
   See also: https://github.com/anerv/BikeDNA
   Building high-quality bicycle networks requires knowledge of existing bicycle infrastructure. However, bicycle network data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality, which hampers the green transition of human mobility. In particular, bicycle-specific data have peculiarities that require a tailor-made, reproducible quality assessment pipeline: For example, bicycle networks are much more fragmented than road networks, or are mapped with inconsistent data models. To fill this gap, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data with a focus on network structure and connectivity. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell level, thus exposing spatial variation in data quality. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications—from urban planning to OpenStreetMap data improvement or network research for sustainable mobility.
2. How Do US Congress Members Advertise Climate Change: An Analysis of Ads Run on Meta’s Platforms, by L. Aisenpreis, G. Gyrst & V. Sekaram published in Proceedings of the International AAAI Conference on Web and Social Media
   
   Ensuring transparency and integrity in political communication on climate change has arguably never been more important than today. Yet we know little about how politicians focus on, talk about, and portray climate change on social media. Here we study it from the perspective of political advertisement. We use Meta’s Ad Library to collect 602,546 ads that have been issued by US Congress members since mid-2018. Out of those only 19,176 (3.2%) are climate-related. Analyzing this data, we find that Democrats focus substantially more on climate change than Republicans, with 99.7% of all climate-related ads stemming from Democratic politicians. In particular, we find this is driven by a small core of Democratic politicians, where 72% of all impressions can be attributed to 10 politicians. Interestingly, we find a significant difference in the average amount of impressions generated per dollar spent between the two parties. Republicans generate on average 188% more impressions with their climate ads for the same money spent as Democrats. We build models to explain the differences and find that demographic factors only partially explain the variance. Our results demonstrate differences of climate-related advertisements of US congress members and reveal differences in advertising characteristics between the two political parties. We anticipate our work to be a starting point for further studies about climate-related ads on Meta’s platforms.
3. Social dimensions impact individual sleep quantity and quality, by S. Park, A. Zhunis, M. Constantinides, L.M. Aiello, D. Quercia & M. Cha, published in Scientific Reports
   
   While sleep positively impacts well-being, health, and productivity, the effects of societal factors on sleep remain underexplored. Here we analyze the sleep of 30,082 individuals across 11 countries using 52 million activity records from wearable devices. Our data are consistent with past studies of gender and age-associated sleep characteristics. However, our analysis of wearable device data uncovers differences in recorded vs. self-reported bedtime and sleep duration. The dataset allowed us to study how country-specific metrics such as GDP and cultural indices relate to sleep in groups and individuals. Our analysis indicates that diverse sleep metrics can be represented by two dimensions: sleep quantity and quality. We find that 55% of the variation in sleep quality, and 63% in sleep quantity, are explained by societal factors. Within a societal boundary, individual sleep experience was modified by factors like exercise. Increased exercise or daily steps were associated with better sleep quality (for example, faster sleep onset and less time awake in bed), especially in countries like the U.S. and Finland. Understanding how social norms relate to sleep will help create strategies and policies that enhance the positive impacts of sleep on health, such as productivity and well-being.

Revealing the determinants of gender inequality in urban cycling with large-scale data, by A. Battiston, L. Napoli, P. Bajardi, A. Panisson, A. Perotti, M. Szell & R. Schifanella, published in EPJ Data Science

The uptake of cycling in today’s cities is especially low for women: there is a largely unexplained, persistent gender gap in cycling. To understand the determinants of this gender gap in cycling at scale, here we use massive, automatically-collected data from the tracking application Strava on outdoor cycling for 61 cities across the United States, the United Kingdom, Italy and the Benelux area. While Strava data is particularly well-suited to describe the behavior of regular cyclists and its generalizability to occasional cyclists requires further investigation, the size of these data and their characteristics represent an unprecedented opportunity for the literature on cycling. Leveraging the associated gender and usage information, we first quantify the emerging gender gap in recreational cycling at city-level. A comparison of cycling rates of women across cities within similar geographical areas—where the penetration of Strava is assumed to be comparable—unveils a broad range of gender gaps. On a macroscopic level, we link this heterogeneity to a variety of urban indicators and provide evidence for traditional hypotheses on the determinants of the gender-cycling-gap. We find a positive association between female cycling rate and urban road safety. On a microscopic level, we identify female preferences for street-specific features in the city of New York. Assuming that the determinants of the gender-cycling-gap are similar across regular and occasional cyclists, our study suggests that enhancing the quality of the dedicated cycling infrastructure may be a way to make urban environments more accessible for women, thereby making urban transport more sustainable for everyone.

Today we published a paper on ideological polarization. Special congrats to Marilena for this being her first paper!

An intensely debated topic is whether political polarization on social media is on the rise. We can investigate this question only if we can quantify polarization, by taking into account how extreme the opinions of the people are, how much they organize into echo chambers, and how these echo chambers organize in the network. Current polarization estimates are insensitive to at least one of these factors: they cannot conclusively clarify the opening question. Here, we propose a measure of ideological polarization which can capture the factors we listed. The measure is based on the Generalized Euclidean (GE) distance, which estimates the distance between two vectors on a network, e.g., representing people’s opinion. This measure can fill the methodological gap left by the state of the art, and leads to useful insights when applied to real-world debates happening on social media and to data from the US Congress.

Multidimensional tie strength and economic development, by L.M. Aiello, S. Joglekar, and D. Quercia, published in Scientific Reports

For decades, Granovetter’s tie strength has been quantified using the frequency of interaction. Yet, frequency does not reflect Granovetter’s initial conception of strength, which is a mix of social dimensions including exchnage of knowledge and provision of support. We used Natural Language Processing to quantify whether text messages convey expressions of knowledge or support, and applied it to a large conversation network from of Reddit users resident in the United States. Borrowing a classic experimental setup, we tested whether the diversity of social connections of Reddit users resident in a specific US state would correlate with the economic opportunities in that state (estimated with GDP per capita). We found that the combination of diversity calculated on the knowledge and support networks correlates much more strongly with GDP than diversity calculated on a network weighted with interaction frequency (R2=0.62 vs. R2=0.30). We also found that the two types of ties differ in their geographical span. Knowledge ties are long-distance (i.e., connecting people living in far-away states), support ties are mostly created among people living close by. Read more in this blogpost.

We published another urban planning paper:

1. Computational Desire Line Analysis of Cyclists on the Dybbølsbro Intersection in Copenhagen, by S.M. Breum, B. Kostic, and M. Szell, published in Transport Findings

   

   To improve intersection planning, here we develop a computational method to detect cyclist trajectories from video recordings and apply it to the Dybbølsbro intersection in Copenhagen, Denmark. In one hour of footage we find hundreds of trajectories that contradict the design, explainable by the desire for straightforward, uninterrupted travel largely not provided by the intersection. This neglect and the prioritization of vehicular traffic highlight opportunities for improving Danish intersection design.

We are on an urban planning streak, publishing two new papers in Environment and Planning B:

1. Urban form and COVID-19 cases and deaths in Greater London: An urban morphometric approach, by A. Venerandi, L.M. Aiello, and S. Porta, published in Environment and Planning B
   
   The COVID-19 pandemic generated a considerable debate in relation to urban density. Many urban planners have advocated for rethinking our cities in ways that can decrease built-up density, in order to curb the spreading of future epidemics. In this work, we show that would be a bad idea. We used urban morphometrics to quantify built-up density in Greater London, and studied its relationship with COVID-19 cases and deaths at the level of MSOAs (small neighborhoods with an average population of ~8000). We found that urban density weakly and negatively correlates with both deaths and cases. The picture above (the low-density areas that some think could save us from contagion) shows the typical area in London with highest occurrence of COVID cases. The widespread belief that COVID cases scale with built-up density was supported mostly by city-level studies. The picture changes when comparing different areas within a city, which has been done for the first time in our study. The moral of the story is that built-up density is different from crowding. Let’s keep that in mind before worsening the urban sprawl of our cities. 
2. Data-driven micromobility network planning for demand and safety, by P. Folco, L. Gauvin, M. Tizzoni, and M. Szell, published in Environment and Planning B
   
   In this paper we study how data of micromobility trips and crashes can shape and automatize infrastructure network planning processes. We introduce a parameter that tunes the focus between demand-based and safety-based development, and investigate systematically this tradeoff for the city of Turin. We find that a full focus on demand or safety generates different network extensions in the short term, with an optimal tradeoff in-between. In the long term, our framework improves overall network quality independent of short-term focus. Thus, we show how a data-driven process can provide urban planners with automated assistance for variable short-term scenario planning while maintaining the long-term goal of a sustainable, city-spanning micromobility network.
   See the interactive visualization: http://www.datainterfaces.org/projects/biketracks/#turin

We have a new NERDS paper out by our Postdoc Sandro Sousa. This paper marks his main work from his PhD at Queen Mary University of London, published now:

1. Quantifying ethnic segregation in cities through random walks by S. Sousa and V. Nicosia, published in Nature Communications
   
   We propose here a family of non-parametric measures for spatial distributions, based on the statistics of the trajectories of random walks on graphs associated to a spatial system. These quantities provide a consistent estimation of segregation in synthetic spatial patterns, and we use them to analyse the ethnic segregation of metropolitan areas in the US and the UK. We show that the spatial diversity of ethnic distributions, as measured through diffusion on graphs, allow us to compare the ethnic segregation of urban areas having different size, shape, or peculiar microscopic characteristics, and exhibits a strong association with socio-economic deprivation.