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

Extended impacts of climate change on health and wellbeing

This is the author’s version of a work that was accepted for publication in Environmental Science and Policy. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published at doi:10.1016/j.envsci.2014.08.011Anthropogenic climate change is progressively transforming the environment despite political and technological attempts to reduce greenhouse gas emissions to tackle global warming. Here we propose that greater insight and understanding of the health-related impacts of climate change can be gained by integrating the positivist approaches used in public health and epidemiology, with holistic social science perspectives on health in which the concept of ‘wellbeing’ is more explicitly recognised. Such an approach enables us to acknowledge and explore a wide range of more subtle, yet important health-related outcomes of climate change. At the same time, incorporating notions of wellbeing enables recognition of both the health co-benefits and dis-benefits of climate change adaptation and mitigation strategies across different population groups and geographical contexts. The paper recommends that future adaptation and mitigation policies seek to ensure that benefits are available for all since current evidence suggests that they are spatially and socially differentiated, and their accessibility is dependent on a range of contextually specific socio-cultural factors.EU FP7 URGENCHE programmeERDFES

High resolution exposure modelling of heat and air pollution and the impact on mortality

Background Elevated temperature and air pollution have been associated with increased mortality. Exposure to heat and air pollution, as well as the density of vulnerable groups varies within cities. The objective was to investigate the extent of neighbourhood differences in mortality risk due to heat and air pollution in a city with a temperate maritime climate. Methods A case-crossover design was used to study associations between heat, air pollution and mortality. Different thermal indicators and air pollutants (PM10, NO2, O3) were reconstructed at high spatial resolution to improve exposure classification. Daily exposures were linked to individual mortality cases over a 15 year period. Results Significant interaction between maximum air temperature (Tamax) and PM10 was observed. During “summer smog” days (Tamax > 25 °C and PM10 > 50 μg/m3), the mortality risk at lag 2 was 7% higher compared to the reference (Tamax 15 °C and PM10 15 μg/m3). Persons above age 85 living alone were at highest risk. Conclusion We found significant synergistic effects of high temperatures and air pollution on mortality. Single living elderly were the most vulnerable group. Due to spatial differences in temperature and air pollution, mortality risks varied substantially between neighbourhoods, with a difference up to 7%