Global, local and focused geographic clustering for case-control data with residential histories

BACKGROUND: This paper introduces a new approach for evaluating clustering in case-control data that accounts for residential histories. Although many statistics have been proposed for assessing local, focused and global clustering in health outcomes, few, if any, exist for evaluating clusters when individuals are mobile. METHODS: Local, global and focused tests for residential histories are developed based on sets of matrices of nearest neighbor relationships that reflect the changing topology of cases and controls. Exposure traces are defined that account for the latency between exposure and disease manifestation, and that use exposure windows whose duration may vary. Several of the methods so derived are applied to evaluate clustering of residential histories in a case-control study of bladder cancer in south eastern Michigan. These data are still being collected and the analysis is conducted for demonstration purposes only. RESULTS: Statistically significant clustering of residential histories of cases was found but is likely due to delayed reporting of cases by one of the hospitals participating in the study. CONCLUSION: Data with residential histories are preferable when causative exposures and disease latencies occur on a long enough time span that human mobility matters. To analyze such data, methods are needed that take residential histories into account

水害に対するコミュニティレジリエンス評価のための地理空間指標

国立大学法人長岡技術科学大

Case-control geographic clustering for residential histories accounting for risk factors and covariates

BACKGROUND: Methods for analyzing space-time variation in risk in case-control studies typically ignore residential mobility. We develop an approach for analyzing case-control data for mobile individuals and apply it to study bladder cancer in 11 counties in southeastern Michigan. At this time data collection is incomplete and no inferences should be drawn – we analyze these data to demonstrate the novel methods. Global, local and focused clustering of residential histories for 219 cases and 437 controls is quantified using time-dependent nearest neighbor relationships. Business address histories for 268 industries that release known or suspected bladder cancer carcinogens are analyzed. A logistic model accounting for smoking, gender, age, race and education specifies the probability of being a case, and is incorporated into the cluster randomization procedures. Sensitivity of clustering to definition of the proximity metric is assessed for 1 to 75 k nearest neighbors. RESULTS: Global clustering is partly explained by the covariates but remains statistically significant at 12 of the 14 levels of k considered. After accounting for the covariates 26 Local clusters are found in Lapeer, Ingham, Oakland and Jackson counties, with the clusters in Ingham and Oakland counties appearing in 1950 and persisting to the present. Statistically significant focused clusters are found about the business address histories of 22 industries located in Oakland (19 clusters), Ingham (2) and Jackson (1) counties. Clusters in central and southeastern Oakland County appear in the 1930's and persist to the present day. CONCLUSION: These methods provide a systematic approach for evaluating a series of increasingly realistic alternative hypotheses regarding the sources of excess risk. So long as selection of cases and controls is population-based and not geographically biased, these tools can provide insights into geographic risk factors that were not specifically assessed in the case-control study design

Decision Model for Predicting Social Vulnerability Using Artificial Intelligence

The APC was funded by their authors.Social vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.This research was funded by the University of Granada, grant number PP2016-PIP0

Deciphering activity patterns using time-geography framework: A case study of Oklahoma State University, Stillwater Campus

Human societies are organized around activities. Every individual participates in certain activities at all times, which are organized in both time and space. Therefore to understand how human societies are organized, it is important to understand how human activities are organized. Traditionally, methods of activity analysis have employed transportation planning, structural equation, simulation and other computational models. Most of these models use trips and trip making as the bases for activity analysis. Current practice however recognizes activities as the focus of activity analysis since trips are derived from the demand of people to participate in activities. This and other shortcomings of the traditional models have resulted in the search for new perspectives and tools to analyze activity patterns. Hagerstrand's time-geography presents an elegant framework to study and understand activity patterns through several important and clearly defined concepts such as stations, space-time paths, space-time prisms, and activity constraints. One of the most important attribute of this framework is its capacity to capture and represent the sequence of human activities in simple but effective ways. The space-time path is a three-dimensional (3D) trajectory that represents the locations of human activities in a two-dimensional (2D) plane and captures the time and sequence of activity participation through the third dimension - time. Activity constraints also provide an understanding of the necessary conditions needed for human activity to take place. Unfortunately, only a few studies have developed methods of activity analysis using this framework. This study adopts the time-geography framework and concepts to develop two new methods to decipher activity patterns. The daily activity schedule fragmentation index (DASFI) examines the propensity of individuals to organize their activities in chains or fragments. The daily activity intensity similarity index (DAISI) measures the degree of similarity between the activity profiles of people. Both indices can be used in cluster analysis to derive clusters which group individuals with similar characteristics in their activity patterns. A case study with the population at Oklahoma State University - Stillwater Campus proves useful in understanding how people organize their activities and could help in planning geographical space to meet the activity needs of people

Contemporary disaster management framework quantification of flood risk in rural Lower Shire Valley, Malawi

Despite floods and droughts accounting for 80% and 70% disaster related deaths and economic loss respectively in Sub-Saharan Africa (SSA), there have been very few attempts in SSA to quantify flood-related vulnerability and risk, especially as they relate to the rural poor. This thesis quantifies and profiles the flood risk of rural communities in SSA focusing on the Lower Shire Valley, Malawi. Given the challenge of hydrometeorological data quality in SSA to support quantitative flood risk assessments, the work first reconstructs and extends hydro-meteorological data using Artificial Neural Networks (ANNs). These data then formed the input to a coupled IPCC-Sustainable Development Frameworks for quantifying flood vulnerability and risk. Flood risk was obtained by integrating hazard and vulnerability. Flood hazard was characterised in terms of flood depth and inundation area obtained through hydraulic modelling of the catchment with Lisflood-FP, while the vulnerability was indexed through analysis of exposure, susceptibility and capacity and linked to social, economic, environmental and physical perspectives. Data on these were collected through structured interviews carried out with the communities and stakeholders in the valley and later analysed. The implementation of the entire analysis within a GIS environment enabled the visualisation of spatial variability in flood risk in the valley. The results show predominantly medium levels in hazardousness, vulnerability and risk. The vulnerability is dominated by a high to very high susceptibility component largely because of the high to very high socio-economic and environmental vulnerability. Economic and physical capacities tend to be predominantly low but social capacity is significantly high, resulting in overall medium levels of capacity-induced vulnerability. Exposure manifests as medium. Both the vulnerability and risk showed marginal spatial variability. Given all this, the thesis argues for the need to mainstream disaster reduction in the rather plethoric conventional socio-economic developmental programmes in SSA. Additionally, the low spatial variability in both the risk and vulnerability in the valley suggests that any such interventions need to be valley-wide to be effective

Exploring dance movement data using sequence alignment methods

Despite the abundance of research on knowledge discovery from moving object databases, only a limited number of studies have examined the interaction between moving point objects in space over time. This paper describes a novel approach for measuring similarity in the interaction between moving objects. The proposed approach consists of three steps. First, we transform movement data into sequences of successive qualitative relations based on the Qualitative Trajectory Calculus (QTC). Second, sequence alignment methods are applied to measure the similarity between movement sequences. Finally, movement sequences are grouped based on similarity by means of an agglomerative hierarchical clustering method. The applicability of this approach is tested using movement data from samba and tango dancers

Collaborative geographic visualization

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente, perfil Gestão e Sistemas AmbientaisThe present document is a revision of essential references to take into account when developing ubiquitous Geographical Information Systems (GIS) with collaborative visualization purposes. Its chapters focus, respectively, on general principles of GIS, its multimedia components and ubiquitous practices; geo-referenced information visualization and its graphical components of virtual and augmented reality; collaborative environments, its technological requirements, architectural specificities, and models for collective information management; and some final considerations about the future and challenges of collaborative visualization of GIS in ubiquitous environment