The interaction between individual,social and environmental factors and their influence on dietary intake among adults in Toronto

Health outcomes related to vegetable and fruit consumption are widely recognized in the literature. This study investigates how dietary intake is influenced by individual, social, and environmental factors in the Toronto Census Metropolitan Area. The analysis and findings are based on data from the Canadian Community Health Survey which provides self‐reported vegetable and fruit intake from 6,513 adults in 2009‐2010. Food environment measures were constructed from commercial databases using kernel density estimates and network drive times. Spatial and multivariable techniques were used to determine the associations between diet, the food environment, and other health and socioeconomic factors. Particular emphasis was given to understanding the interaction between the food environment and socioeconomic position. Unexpectedly, supermarket density was found to have an inverse association with vegetable and fruit intake. Interaction terms for individuals with low income and reduced mobility produced different responses in men and women, confirming that the influence of the food environment is not uniform for all subgroups.     

Shapes on a plane: evaluating the impact of projection distortion on spatial binning

ABSTRACT

One method for working with large, dense sets of spatial point data is to aggregate the measure of the data into polygonal containers, such as political boundaries, or into regular spatial bins such as triangles, squares, or hexagons. When mapping these aggregations, the map projection must inevitably distort relationships. This distortion can impact the reader’s ability to compare count and density measures across the map. Spatial binning, particularly via hexagons, is becoming a popular technique for displaying aggregate measures of point data sets. Increasingly, we see questionable use of the technique without attendant discussion of its hazards. In this work, we discuss when and why spatial binning works and how mapmakers can better understand the limitations caused by distortion from projecting to the plane. We introduce equations for evaluating distortion’s impact on one common projection (Web Mercator) and discuss how the methods used generalize to other projections. While we focus on hexagonal binning, these same considerations affect spatial bins of any shape, and more generally, any analysis of geographic data performed in planar space.     

Optimal Stopping Location of a High Speed Train using GIS and Multicriteria Decision‐making

This article develops a methodology using a Geographical Information System (GIS) to evaluate the best location to stop a high speed passenger train when faced with an undesired event. The proposed method is based on multicriteria decision‐making where different stretches of line which could be chosen as the stopping point are ranked depending on the characteristics of the line, the surrounding area and its accessibility for equipment. The method was integrated into the GIS to develop an expert support system for decision makers faced with different kinds of undesired events. It has been applied to a case study on the high speed line between Valladolid and Madrid (Spain). The proposed method is new and has not previously been applied to high speed railway networks and could be adapted to other case studies. The speed of the algorithm provides an almost instantaneous reply within seconds of an emergency situation occurring. The method can therefore be part of an overall support system for decision making in undesired rail events.     

干涉合成孔径雷达及其在火山研究中的应用(第二部分:阿拉斯加火山的InSAR成像)(英文)

干涉合成孔径雷达是一项能够对地表变形进行测量的遥感技术,在一个巨大区域内,它的变形测量精度可达亚厘米级,而其空间分辨率则在数十米以内。本文对我们应用InSAR技术对阿拉斯加火山进行研究的最新成果进行了总结,同时包括火山的喷发和非喷发活动。这些研究成果表明InSAR不仅能够增进我们对阿拉斯加火山活动的理解,而且还能够增强我们对未来火山喷发情况及其灾害的预测能力。     

The influence of sampling design on spatial data quality in a geographic citizen science project

Geographic citizen science has much potential to assist in wildlife research and conservation, but the quality of observation data is a key concern. We examined the effects of sampling design on the quality of spatial data collected for a koala citizen science project in Australia. Data were collected from three samples—volunteers (n = 454), an Internet panel (n = 103), and landowners (n = 35)—to assess spatial data quality, a dimension of citizen science projects rarely considered. The locational accuracy of koala observations among the samples was similar when benchmarked against authoritative data (i.e., an expert‐derived koala distribution model), but there were differences in the quantity of data generated. Fewer koala location data were generated per participant by the Internet panel sample than the volunteer or landowner samples. Spatial preferences for land uses affecting koala conservation were also mapped, with landowners more likely to map locations for residential and tourism development and volunteers less likely. These spatial preferences have the potential to influence the social acceptability of future koala conservation proposals. With careful sampling design, both citizen observations and land use preferences can be included within the same project to augment scientific assessments and identify conservation opportunities and constraints.     

Spatiotemporal variations in heat-related health risk in three Midwestern US cities between 1990 and 2010

Mortality from extreme heat is a leading cause of weather-related fatality, which is expected to increase in frequency with future climate scenarios. This study examines the spatiotemporal variations in heat-related health risk in three Midwestern cities in the USA between the years 1990 to 2010; cities include Chicago, Illinois, Indianapolis, IN and Dayton, OH. In order to examine these variations, we utilize the recently developed extreme heat vulnerability index (EHVI) that uses a principal components solution to vulnerability. The EHVI incorporates data from the US Decadal Census and remotely sensed variables to determine heat-related vulnerability at an intra-urban level (census block group). The results demonstrate significant spatiotemporal variations in heat health risk within the cities involved.     

Multivariate Analysis of Incomplete Mapped Data

Classical multivariate analyses are based on matrix algebra and enable the analysis of a table containing measurements of a set of variables for a set of sites. Incomplete mapped data consist of measurements of a set of variables recorded for the same geographical region but for different zonal systems and with only a partial sampling of this zone. This kind of data cannot be analysed with usual multivariate methods because there is no common system of sites for all variables. We propose a new approach using GIS technology and NIPALS, an iterative multivariate method, to analyse the spatial patterns of this kind of data. Moreover, an extension of our method is that it can be used for areal interpolation purposes. We illustrate the method in analysing data concerning the distribution of roe deer weights over several years in a reserve.