SearcharxivSearch

arXiv subjects

Howard Wong

Publications and source records attributed to Howard Wong.

2 recordsLinked to original sources

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

The temporal concentration of travel demand in an urban transport network

Suppose $A$ and $B$ are two stations within the mass rapid transit network of a city. Both stations see approximately the same average daily number of passengers entering and exiting their gates. However, passengers are evenly distributed at $A$, whereas activity is concentrated mainly during peak hours at $B$. Although the daily travel demand is the same for both stations, $B$ requires more resources since the number of vehicles, station dimensions and staffing level must be tailored to meet the demands of peak hours. This hypothetical scenario underscores the need to quantify the concentration of travel demand for optimising resource allocation and planning efficiency in an urban transport network. To this end, we introduce a novel metric for assessing the temporal concentration of travel demand at different locations in a generic transport network. Our approach is validated using granular data sourced from smart travel cards, encompassing 272 London Underground (LU) stations. Additionally, we present a methodological framework based on Random Forests to identify attributes of the locations of interest within the transport network that contribute to varying levels of temporal concentration of travel demand. Our case study unveils that LU stations located in areas characterised by low residential, retail, and employment density, predominantly situated in outer London, exhibit the most pronounced temporal concentration of travel demand. Conversely, within inner London, stations servicing high-density employment zones, especially around the City of London, experience a greater temporal concentration of travel demand compared to those catering to commercial and residential districts, typically situated in West London.

physics.soc-ph