arXiv subjects

Carmen Cabrera

Publications and source records attributed to Carmen Cabrera.

8 recordsLinked to original sources

arXiv2026

The scales of urban mobility shape socioeconomic mixing

Urban daily mobility determines how far we travel and whom we encounter through the city. As local-living and polycentric planning gain popularity, a key question is whether more localised mobility can preserve the socioeconomic mixing that cities enable. Yet systematic evidence on how different scales of movement contribute to experienced social exposure remains limited. Here, we use anonymised GPS traces from ~20 million devices across 226 functional urban areas in France, Germany and the UK to decompose everyday mobility into three scales - proximal, medial and distal - and link these to the socioeconomic composition of destinations. Proximal trips remain similar in length across neighbourhoods, whereas medial and distal trips lengthen toward the periphery. Distal trips account for 33% of travel but facilitate 42% of inter-group encounters; reallocating them to local movement reduces experienced mixing by up to one third. These findings reveal a trade-off between shorter travel and integrated urban living.

physics.soc-ph↗

arXiv2026

Hate speech toward migrants on a citizen reporting platform concentrates in neighborhoods undergoing demographic change

Understanding when migration generates social integration or exclusion is a central challenge for urban communities. Existing research has mostly relied on surveys, administrative data, or aggregate indicators that fail to capture expressions of exclusion at fine spatiotemporal scales. Here, we analyze over 550,000 geolocated reports from SOSAFE (Chile's largest citizen reporting platform) to examine the relationship between migration and hate speech in Santiago. We fine-tune a Spanish hate speech classifier and validate it against human labels. Reports that mention migrants are more likely to contain hate speech than other reports. Hate speech concentrates in areas with recent demographic change (post-2010 arrivals) rather than in established migrant communities. The spatial analysis shows that hate speech hotspots coincide with neighborhoods where recent migrants comprise over a third of the population. Coldspots appear in high-education sectors with minimal recent migration. Reports with hate speech and reports that mention migrants receive more engagement, although their combination is not amplified further. These results show digital bordering on a citizen reporting platform: exclusionary discourse concentrates, and receives more engagement, in neighborhoods undergoing recent demographic change.

cs.SI↗

arXiv2026

Conflict, mobility and fragmentation in the African interurban network

Roads make regional integration possible, but they also concentrate risk. When violence reaches a corridor that carries regional movement, the consequences can extend beyond the attacked place. We study this problem in Africa by combining interurban road data, georeferenced conflict events, and bilateral migration flows. Using a Hawkes-style self-exciting memory kernel, we estimate accumulated conflict intensity near roads from 2019 to 2022, and we route modelled movement between cities along the road network. As an empirical check on these modelled corridor flows, we show that country pairs connected by routes with higher accumulated conflict have lower bilateral migration. The association remains negative across alternative assumptions about spatial decay and is strongest for paths that cross regions, consistent with a corridor-deterrence channel rather than only displacement from violent places. We then use cascade and percolation simulations to examine how local disruptions propagate through the road network. Most simulated disruptions isolate relatively small populations, but 18.6% isolate more than one million people, and the largest isolates 13.3 million. Removing roads in order of conflict intensity reveals a tipping point, at which the giant connected component falls from 85% to 39% of the network. Our results identify the roads where violence most threatens regional integration and show how past conflict, mobility, and network structure combine to create persistent corridor vulnerability.

physics.soc-ph↗

arXiv2026

Spatial accessibility to food banks hinders food parcel uptake in England and Wales, particularly in rural areas

Food bank use in the UK has soared in recent years. The combination of a global pandemic, over-stretched and underfunded public services, and a cost-of-living crisis has meant that millions of people cannot afford basic essentials such as food, heating, housing, and baby supplies. Food bank use is driven by a complex range of factors, including poverty, health emergencies, income shocks, delays to universal credit payments, housing issues, and homelessness. In this study we identify an urban-rural divide in spatial accessibility to food banks. In cities, food banks tend to be highly accessible by public transport to deprived populations but, on average, have shorter opening hours. In rural areas, however, despite generally longer opening hours, food banks are typically not highly accessible except for the most deprived residents. This matters. We find that spatial accessibility to a Trussell food bank centre is a key predictor of food parcel uptake, with a significantly stronger relationship than factors emphasised in the literature such as disability and Universal Credit. Importantly, this relationship is markedly stronger for rural populations, suggesting an unmet need in deprived rural areas far from food banks. Our work has important implications for food bank policy, suggesting a need for improved public transport in rural areas, and optimising current food bank locations and delivery models.

econ.GN↗

arXiv2026

One country, multiple portraits: representativeness in GPS-based mobility data is source-specific and spatially dependent

Anonymised GPS-based mobile phone data are increasingly used to estimate population distribution and human mobility, supporting applications across disaster response, public health, urban planning and migration research. Yet whether these data fairly represent the populations they describe, particularly outside high-income countries, remains poorly understood. We quantify coverage bias for 2,478 municipalities in Mexico by comparing population estimates from a single-platform source (Facebook) and a multi-app aggregator (Veraset) against the 2020 Mexican Population Census. We find that the magnitude and spatial distribution of coverage bias differ substantially across sources. Facebook provides higher and more evenly distributed coverage, whereas the multi-app data concentrate users in larger, wealthier and more digitally connected places. Coverage bias is also spatially structured, with neighbouring municipalities showing similar levels of over- or under-coverage. Using explainable machine learning, we show that digital access and material resources are the dominant drivers of bias for the multi-app data, while demographic and population structure dominate for Facebook. Explicitly modelling spatial dependence improves the performance of statistical models for explaining bias and reveals that an appreciable share of spatial variation remains unexplained by observed covariates. These findings show that coverage bias is source-specific and spatially dependent, and provide a foundation for adjustments that improve the representativeness of mobile phone data in unequal, data-scarce settings.

physics.soc-ph↗

arXiv2025

A systematic machine learning approach to measure and assess biases in mobile phone population data

Traditional sources of population data, such as censuses and surveys, are costly, infrequent, and often unavailable in crisis-affected regions. Mobile phone application data offer near real-time, high-resolution insights into population distribution, but their utility is undermined by unequal access to and use of digital technologies, creating biases that threaten representativeness. Despite growing recognition of these issues, there is still no standard framework to measure and explain such biases, limiting the reliability of digital traces for research and policy. We develop and implement a systematic, replicable framework to quantify coverage bias in aggregated mobile phone application data without requiring individual-level demographic attributes. The approach combines a transparent indicator of population coverage with explainable machine learning to identify contextual drivers of spatial bias. Using four datasets for the United Kingdom benchmarked against the 2021 census, we show that mobile phone data consistently achieve higher population coverage than major national surveys, but substantial biases persist across data sources and subnational areas. Coverage bias is strongly associated with demographic, socioeconomic, and geographic features, often in complex nonlinear ways. Contrary to common assumptions, multi-application datasets do not necessarily reduce bias compared to single-app sources. Our findings establish a foundation for bias assessment standards in mobile phone data, offering practical tools for researchers, statistical agencies, and policymakers to harness these datasets responsibly and equitably.

stat.AP↗

arXiv2025

Sustained changes to urban mobility after COVID-19 amplified socio-economic inequalities in Latin America

Urban mobility is central to economic activity, social inclusion, and access to essential services. COVID-19 caused disruptions to mobility globally, yet its long-term impacts in less developed countries remain poorly understood. Using over 170 million anonymised mobile phone records from Meta-Facebook users in Argentina, Chile, and Colombia (March 2020 to May 2022), we find sustained changes in mobility across socioeconomic and rural-urban gradients. We reveal that mobility recorded the sharpest declines and remained below pre-pandemic levels in most high-density and low socio-economic deprivation areas, while low-density and more deprived communities returned to baseline. These differences reflect the scale of the initial mobility shock rather than subsequent recovery rates. Net mobility to urban cores remained consistently below pre-pandemic levels, suggesting a shift in their functional role. By revealing how COVID-19 reinforced mobility-related inequalities, we contribute novel evidence for planners and policymakers seeking to build more inclusive and resilient mobility systems.

physics.soc-ph↗

arXiv2025

Extreme heat reduces and reshapes urban mobility

Extreme heat is a problem in European countries and cities, with rising temperatures affecting ageing populations. Research on mobility during extreme heat remains limited to small samples and isolated contexts, leaving significant gaps in our understanding how entire populations adjust their day-to-day activities and how these adaptations vary across social groups. Here we use data from passive and active mobile network connections covering 13 million individuals in Spain (27% of the population) to examine extreme heat's impact on mobility at scale. We stratify by age, gender, economic class, and activity. Our findings show mobility falls by as much as 10% on hot days generally and 20% on hot afternoons specifically, when temperatures peak. Further differences emerge on hot days. Older adults cut travel to work and other activities, while those earning less are less able to avoid work; social mixing declines and spatial structure changes as activity falls in city centres. These disruptions have implications for urban economies, as curbed activity and interaction - both planned and unplanned - threaten the dynamism of cities as hubs of social and economic exchange.

physics.soc-ph↗