Network-based space-time scan statistics for detecting micro-scale hotspots

Events recorded in urban areas are often confined by the micro-scale geography of street networks, yet existing spatial–analytical methods do not usually account for the shortest-path distance of street networks. We propose space–time NetScan, a new spatial–temporal analytical method with improved accuracy for detecting patterns of concentrations across space and time. It extends the notion of a scan-statistic-type search window by measuring space-time patterns along street networks in order to detect micro-scale concentrations of events at the street-address level with high accuracy. Performance tests with synthetic data demonstrate that space-time NetScan outperforms existing methods in detecting the location, shape, size and duration of hotspots. An empirical study with drug-related incidents shows how space-time NetScan can improve our understanding of the micro-scale geography of crime. Aside from some abrupt one-off incidents, many hotspots form recurrent hotbeds, implying that drug-related crimes tend to persist in specific problem places

The impact of fast-food density on obesity during the COVID-19 lockdown in the UK : a multi-timepoint study on British cohort data

Poor food environments are considered to trigger obesity and related health complications by restricting the local food options to predominantly low quality, energy-dense foods. This study investigated the impact of the food environment on obesity with a focus on any changes that might have occurred around the COVID lockdown period in the UK when majority of the population relied on food delivery and the local food environments. The proportion of fast-food retailers in the area and the Retail Food Environment Index (RFEI) were calculated for participants of the 1970 British Cohort Study (BCS70) at three timepoints: pre-COVID (2016), the first UK nation-wide lockdown (April–May 2020) and post lockdown (September–October 2020). The association of the food environment and the odds of obesity was estimated through multivariable logistic regression, with adjustments being made for selected socioeconomic variables. A model using the fast-food proportion as the sole predictor estimated that higher fast-food proportion increased the odds of obesity by 2.41 in 2016, 2.89 during the lockdown and 1.34 post lockdown, compared with 1.87, 2.23, and 0.73, respectively, for the same three periods with adjustments being made for select socioeconomic variables. On the other hand, RFEI increased the odds of obesity only slightly at 1.01, 1.02 and 1.03, respectively, with the model with adjustments yielding respective similar values. The fast-food proportion model indicates that proximity to a poor food environment is linked to obesity, especially during the COVID lockdown period, but the impact of a poor-food environment is limited if the RFEI is used as its indicator. The findings will add much needed insights on the UK data and will inform public health planning and policy

Detection of irregular-shaped clusters on a network by controlling the shape compactness with a penalty function

Recent development of cluster detection methods focuses on the improvement of efficiency or accuracy, with the latter yielding a wide range of variants in the shape of the search window, from a simple circle and elliptic shape to more irregular shapes. Detection of irregular-shaped clusters has seen various new approaches as it is considered to capture the shape and extent of clusters more accurately. One of these newly developed approaches achieves the irregularity of the clusters by placing a penalty on the shape complexity of a candidate cluster. This study extends this approach and applies it to a network-space to detect irregular-shaped clusters along a street network segments in a small urban area. The study uses a genetic algorithm to search candidate clusters and identify the most likely cluster using the framework of spatial scan-statistics. Application of the method to a small synthetic data and a real data set revealed that providing options of different cluster patterns with different compactness parameters helps find more accurate as well as geometrically and contextually more meaningful clusters, as opposed to those detected without a shape controlling parameter

Colocations of spatial clusters among different industries

Spatial colocation have been studied in many contexts including locations of urban facilities, industry entities and businesses. However, identifying colocations among a small number of facilities and establishments holds the risk of introducing false positive in that such a spatial arrangement may have occurred by chance. To account for the association between a group of facilities that frequently colocate with each other, this study proposes a two-step approach consisting of identifying statistically significant clusters of each facility type using the False Discovery Rate (FDR) controlling procedure, and subsequently measuring the colocation of those clusters with the frequent-pattern-growth (FP-growth) algorithm. Empirical analysis of 6 million business and industrial establishments across Japan suggests that 10 out of 86 industry types form clear colocations and their colocations form a multi-layered, cascading structure. The number of layers in the multi-layered structure reflect the city size and the strength of the association between the colocated clusters of industries. These patterns illustrate the utility of detecting colocation of clusters towards understanding the agglomeration of different businesses. The proposed method can be applied to other contexts that would benefit from investigations into how different types of spatial features can be linked with each other and how they form colocations

Evaluation of a team-based collection and delivery operation

The rise in the volume of e-commerce is adding increasing pressure on the logistics of parcel delivery. To improve the efficiency of their operations, the parcel industry in Japan is exploring team-based collection and delivery (TCD), whereby the sales driver (SD) hands out parcels to the field crews (FC), who subsequently deliver them to the door. However, the efficiency of TCD is still understudied. This study proposes a method for optimizing the delivery route for TCD and evaluates the efficiency of the ongoing operation. The TCD delivery problem focuses on minimizing the task completion time using parameters derived through surveys of the actual operations. Comparison between seven different methods show that the newly proposed method of fuzzy c-means clustering with a genetic algorithm outperforms the rest, rapidly computing sufficiently accurate results. Results suggest that the proposed optimal route reduces the total delivery time by up to 18.7%. However, the amount of time saved varies considerably by the area and the number of parcels delivered. Additional constraints for improving driver safety, the cost-benefit of increasing FCs, and the impact on the environmental cost are also considered. The proposed method also helps spread the workload and the travel time of the FCs more evenly, thus further reducing the total delivery time

Biodiversity and the recreational value of green infrastructure in England

Green infrastructure refers to connected corridors of greenspaces within and beyond urban areas. It provides sustainable ecosystem goods and services for people and wildlife, enhancing their wellbeing and protecting them against climatic extremes. However, the exact contributing factors to the betterment of green infrastructure are not systematically examined at a national level. This study aims to identify what helps improve biodiversity and the recreational value of green infrastructure. The study uses hotspot analysis, ordinary least squares (OLS) regression and geographically weighted regression (GWR) to understand the spatial patterns of the relevant variables and outcomes. Findings suggest that high wildlife species richness was reported in Forestry Commission woodlands and country parks, whilst doorstep greens and village greens returned poor species richness. The recreational value of greenspace was affected the most by certain types of greenspace (e.g., woodlands) as well as the percentage of urban cover. They indicate that biodiversity is generally high in areas away from urban centres, while access to greenspace in an urban space brings us high recreational value. These results indicate that green infrastructure is a complex system that requires the right balance between different priorities and services

Application of geospatial analysis in health research: a systematic review of methodological aspects of studies on violence against children and young people

Background: Geographical variation exists in violence experienced by children and young people; however, there is limited research applying geospatial techniques to study this variation, and the methodological quality of this body of work is unclear. Objective: This study aimed to review the application of geospatial analysis in research on violence against children (VAC) and evaluate how essential methodological aspects are reported. Methods: Twelve databases were searched for studies on VAC using geospatial techniques. Two independent reviewers screened the papers for eligibility. Findings were narratively synthesised. Results: Sixty studies were included. Six studies estimated the prevalence of VAC and 54 investigated the associations between VAC and covariates. Most studies were conducted in the US (68 %), and the broad definition of ‘child maltreatment’ (53 %) was the most common form of violence explored. Most studies (83 %) used administrative data, whereas 23 % used an ecological study design to estimate the associations between risk factors and official reports of VAC. Frequentist modelling approaches were used in 54 % of the studies, and 47 % investigated VAC at census tract level. Model fit metrics were reported in 69 % of studies. Conclusions: Current knowledge of the geographical distribution of VAC is severely limited because of the reliance on administrative data, which vastly underestimates the prevalence of VAC compared with self-reports and poor reporting of quality characteristics. There is a huge opportunity for applying geospatial methods in VAC research in diverse geographic contexts. Future research must adopt rigorous and standardised approaches to model fitting and validation and make better use of self-reported data

Road junction configurations and the severity of traffic accidents in Japan

In many countries, 40–60% of the traffic accidents occur at junctions, making the reduction of junction accidents paramount to achieving UN Sustainable Development Goals. In Japan, the road safety guidelines specify the proximity between junctions and non-perpendicular angles at junctions as the two main risk factors behind junction accidents, yet their impact remains understudied. Using binomial logistic regression models, this study investigates the impact of junction intervals and junction angles on the severity of traffic accidents. The study found that, in general, (1) shorter intervals between adjacent junctions helps reduce the risk of serious accidents, which is the opposite of the current road safety guidelines in Japan, and (2) results from the junction angle analysis were mixed but there was no evidence that the roads should meet at a right angle to reduce traffic accidents. Some types of accidents also returned a non-linear curve, e.g., vehicle-to-vehicle collisions at four-armed junctions involving a driver aged 65 years and over have the highest risk of fatal/serious accidents when adjacent junctions were 32 m apart, and the risk reduces at a shorter or longer interval. These results suggest that the current road safety guidelines require updating to improve road safety around junctions

The mortality rates and the space-time patterns of John Snow’s cholera epidemic map

Background Snow’s work on the Broad Street map is widely known as a pioneering example of spatial epidemiology. It lacks, however, two significant attributes required in contemporary analyses of disease incidence: population at risk and the progression of the epidemic over time. Despite this has been repeatedly suggested in the literature, no systematic investigation of these two aspects was previously carried out. Using a series of historical documents, this study constructs own data to revisit Snow’s study to examine the mortality rate at each street location and the space-time pattern of the cholera outbreak. Methods This study brings together records from a series of historical documents, and prepares own data on the estimated number of residents at each house location as well as the space-time data of the victims, and these are processed in GIS to facilitate the spatial-temporal analysis. Mortality rates and the space-time pattern in the victims’ records are explored using Kernel Density Estimation and network-based Scan Statistic, a recently developed method that detects significant concentrations of records such as the date and place of victims with respect to their distance from others along the street network. The results are visualised in a map form using a GIS platform. Results Data on mortality rates and space-time distribution of the victims were collected from various sources and were successfully merged and digitised, thus allowing the production of new map outputs and new interpretation of the 1854 cholera outbreak in London, covering more cases than Snow’s original report and also adding new insights into their space-time distribution. They confirmed that areas in the immediate vicinity of the Broad Street pump indeed suffered from excessively high mortality rates, which has been suspected for the past 160 years but remained unconfirmed. No distinctive pattern was found in the space-time distribution of victims’ locations. Conclusions The high mortality rates identified around the Broad Street pump are consistent with Snow’s theory about cholera being transmitted through contaminated water. The absence of a clear space-time pattern also indicates the water-bourne, rather than the then popular belief of air bourne, nature of cholera. The GIS data constructed in this study has an academic value and would cater for further research on Snow’s map