Open-Source web-based geographical information system for health exposure assessment

This paper presents the design and development of an open source web-based Geographical Information System allowing users to visualise, customise and interact with spatial data within their web browser. The developed application shows that by using solely Open Source software it was possible to develop a customisable web based GIS application that provides functions necessary to convey health and environmental data to experts and non-experts alike without the requirement of proprietary software

On the move:Exploring the impact of residential mobility on cannabis use

AbstractA large literature exists suggesting that residential mobility leads to increased participation in risky health behaviours such as cannabis use amongst youth. However, much of this work fails to account for the impact that underlying differences between mobile and non-mobile youth have on this relationship. In this study we utilise multilevel models with longitudinal data to simultaneously estimate between-child and within-child effects in the relationship between residential mobility and cannabis use, allowing us to determine the extent to which cannabis use in adolescence is driven by residential mobility and unobserved confounding. Data come from a UK cohort, The Avon Longitudinal Study of Parents and Children. Consistent with previous research we find a positive association between cumulative residential mobility and cannabis use when using multilevel extensions of conventional logistic regression models (log odds: 0.94, standard error: 0.42), indicating that children who move houses are more likely to use cannabis than those who remain residentially stable. However, decomposing this relationship into within- and between-child components reveals that the conventional model is underspecified and misleading; we find that differences in cannabis use between mobile and non-mobile children are due to underlying differences between these groups (between-child log odds: 3.56, standard error: 1.22), not by a change in status of residential mobility (within-child log odds: 1.33, standard error: 1.02). Our findings suggest that residential mobility in the teenage years does not place children at an increased risk of cannabis use throughout these years

Spatial and Temporal Geovisualisation and Data Mining of Road Traffic Accidents in Christchurch, New Zealand

Abstract This paper outlines the development of a method for using Kernel Estimation cluster analysis techniques to automatically identify road traffic accident 'black spots' and 'black areas'. A Novel data-mining approach has been developed -adding to the generic exploratory spatial analysis toolkit. Christchurch, New Zealand, was selected as the study area and data from the LTNZ crash database was used to trial the technique. A GIS and Python scripting was used to implement the solution, combining spatial data for average traffic flows with the recorded accident locations. Kernel Estimation was able to identify the accident clusters, and when used in conjunction with Monte Carlo simulation techniques, was able to identify statistically significant clusters

National movement patterns during the COVID-19 pandemic in New Zealand:The unexplored role of neighbourhood deprivation

BACKGROUND: The COVID-19 pandemic has asked unprecedented questions of governments around the world. Policy responses have disrupted usual patterns of movement in society, locally and globally, with resultant impacts on national economies and human well-being. These interventions have primarily centred on enforcing lockdowns and introducing social distancing recommendations, leading to questions of trust and competency around the role of institutions and the administrative apparatus of state. This study demonstrates the unequal societal impacts in population movement during a national ‘lockdown’. METHODS: We use nationwide mobile phone movement data to quantify the effect of an enforced lockdown on population mobility by neighbourhood deprivation using an ecological study design. We then derive a mobility index using anonymised aggregated population counts for each neighbourhood (2253 Census Statistical Areas; mean population n=2086) of national hourly mobile phone location data (7.45 million records, 1 March 2020–20 July 2020) for New Zealand (NZ). RESULTS: Curtailing movement has highlighted and exacerbated underlying social and spatial inequalities. Our analysis reveals the unequal movements during ‘lockdown’ by neighbourhood socioeconomic status in NZ. CONCLUSION: In understanding inequalities in neighbourhood movements, we are contributing critical new evidence to the policy debate about the impact(s) and efficacy of national, regional or local lockdowns which have sparked such controversy

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both the dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images.Comment: Accepted manuscript including appendix (supplementary file

Health impact assessment of transport policies in Rotterdam:Decrease of total traffic and increase of electric car use

BACKGROUND: Green house gas (GHG) mitigation policies can be evaluated by showing their co-benefits to health.METHOD: Health Impact Assessment (HIA) was used to quantify co-benefits of GHG mitigation policies in Rotterdam. The effects of two separate interventions (10% reduction of private vehicle kilometers and a share of 50% electric-powered private vehicle kilometers) on particulate matter (PM2.5), elemental carbon (EC) and noise (engine noise and tyre noise) were assessed using Years of Life Lost (YLL) and Years Lived with Disability (YLD). The baseline was 2010 and the end of the assessment 2020.RESULTS: The intervention aimed at reducing traffic is associated with a decreased exposure to noise resulting in a reduction of 21 (confidence interval (CI): 11-129) YLDs due to annoyance and 35 (CI: 20-51) YLDs due to sleep disturbance for the population per year. The effects of 50% electric-powered car use are slightly higher with a reduction of 26 (CI: 13-116) and 41 (CI: 24-60) YLDs, respectively. The two interventions have marginal effects on air pollution, because already implemented traffic policies will reduce PM2.5 and EC by around 40% and 60% respectively, from 2010 to 2020.DISCUSSION: The evaluation of planned interventions, related to climate change policies, targeting only the transport sector can result in small co-benefits for health, if the analysis is limited to air pollution and noise. This urges to expand the analysis by including other impacts, e.g. physical activity and well-being, as a necessary step to better understanding consequences of interventions and carefully orienting resources useful to build knowledge to improve public health.</p