Is There Really a “Wrong Side of the Tracks”in Urban Areas and Does It Matter for Spatial Analysis?

Urban highways are barriers to social ties

Urban highways are common, especially in the United States, making cities more car-centric. They promise the annihilation of distance but obstruct pedestrian mobility, thus playing a key role in limiting social interactions locally. Although this limiting role is widely acknowledged in urban studies, the quantitative relationship between urban highways and social ties is barely tested. Here, we define a Barrier Score that relates massive, geolocated online social network data to highways in the 50 largest US cities. At the granularity of individual social ties, we show that urban highways are associated with decreased social connectivity. This barrier effect is especially strong for short distances and consistent with historical cases of highways that were built to purposefully disrupt or isolate Black neighborhoods. By combining spatial infrastructure with social tie data, our method adds a dimension to demographic studies of social segregation. Our study can inform reparative planning for an evidence-based reduction of spatial inequality, and more generally, support a better integration of the social fabric in urban planning.

Land-Use Changes and Socioeconomic Conditions of Communities along the Carajás Railroad in Eastern Amazonia

Studies on communities along railroads are relevant because of the impacts of operations on their socioeconomic and environmental conditions. The objective of this paper was to examine 32 communities affected by the Carajas railroad between 2010 and 2017. The socioeconomic and environmental dimensions involved an examination of 17 indicators and a qualitative analysis of Google Earth images, respectively. We applied appropriate statistical tests for data analysis. The results showed that urban communities have better socioeconomic conditions (higher incomes and less poverty) than rural ones; and that those in Maranhão are in a less sustainable situation (poor socioeconomic indicators and significant changes in land cover). The communities near the railroad showed higher variation in socio-environmental conditions. We conclude that socioeconomic and satellite images data analysis can help increase the resilience and sustainability of communities in risk situations. The latter include people with few financial resources, poor housing conditions, and living in areas with significant land cover changes because they provide fundamental data and information that can guide action through the formulation and execution of public policies or decision-making by other agents, such as private companies. Therefore, policymakers, managers, and other stakeholders should focus on community-scale deficiencies, especially in identified priority communities.

Spatio-temporal disease risk estimation using clustering-based adjacency modelling

Conditional autoregressive models are typically used to capture the spatial autocorrelation present in areal unit disease count data when estimating the spatial pattern in disease risk. This correlation is represented by a binary neighbourhood matrix based on a border sharing specification, which enforces spatial correlation between geographically neighbouring areas. However, enforcing such correlation will mask any discontinuities in the disease risk surface, thus impeding the detection of clusters of areas that exhibit higher or lower risks compared to their neighbours. Here we propose novel methodology to account for these clusters and discontinuities in disease risk via a two-stage modelling approach, which either forces the clusters/discontinuities to be the same for all time periods or allows them to evolve dynamically over time. Stage one constructs a set of candidate neighbourhood matrices to represent a range of possible cluster/discontinuity structures in the data, and stage two estimates an appropriate structure(s) by treating the neighbourhood matrix as an additional parameter to estimate within a Bayesian spatio-temporal disease mapping model. The effectiveness of our novel methodology is evidenced by simulation, before being applied to a new study of respiratory disease risk in Greater Glasgow, Scotland from 2011 to 2017.

Exploring the neighbourhood-level correlates of Covid-19 deaths in London using a difference across spatial boundaries method

This paper explores neighbourhood-level correlates of the Covid-19 deaths in London during the initial rise and peak of the pandemic within the UK - the period March 1 to April 17, 2020. It asks whether the person-level predictors of Covid-19 that are identified in reports by Public Health England and by the Office of National Statistics also hold at a neighbourhood scale, remaining evident in the differences between neighbours. In examining this, the paper focuses on localised differences in the number of deaths, putting forward an innovative method of analysis that looks at the differences between places that share a border. Specifically, a difference across spatial boundaries method is employed to consider whether a higher number of deaths in one neighbourhood, when compared to its neighbours, is related to other differences between those contiguous locations. It is also used to map localised 'hot spots' and to look for spatial variation in the regression coefficients. The results are compared to those for a later period, April 18 - May 31. The findings show that despite some spatial diffusion of the disease, a greater number of deaths continues to be associated with Asian and Black ethnic groups, socio-economic disadvantage, very large households (likely indicative of residential overcrowding), and fewer from younger age groups. The analysis adds to the evidence showing that age, wealth/deprivation, and ethnicity are key risk factors associated with higher mortality rates from Covid-19.