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Background: Chronic obstructive pulmonary disease (COPD) is the second leading cause of death in India. The objective of this study was to map COPD cases and its risk factors and to determine the association between them using geographic information system (GIS) in a semi-urban area of Trivandrum, South India., Materials and Methods: This community-based cross-sectional, descriptive study (n = 494) was conducted in a subcenter area of a primary health center. Location was mapped and COPD population screener questionnaire was administered to all the study subjects enrolled by census method. Lifetime firewood exposure (person-hours) and tobacco smoking were enquired and distance from road was mapped using portable differential global positioning system. The association with COPD was assessed by kriging and hotspot analysis using ArcGIS software., Results: The prevalence of COPD (6.5%) was comparable to national prevalence estimates. Spatial maps showed COPD case clustering in areas with higher firewood exposure, greater smoking exposure, and in households with closer proximity to local roads. A particular high-risk cluster was obtained which had a significant association with all the risk factors., Conclusion: GIS technology is useful in identification of spatial clustering of COPD cases and its environmental risk factors, making it an important tool for targeted interventions for COPD., Competing Interests: None

The understanding of the regional and local dimensions of vulnerability due to climate change is essential to develop appropriate and targeted adaptation efforts. We assessed the local dimensions of vulnerability in the tropical state of Kerala, India, using a purposely developed vulnerability index, which accounts for both environmental and socio-economic factors. The large extents of coastal wetlands and lagoons and high concentration of mangrove forests make the state environmentally vulnerable. Low human development index, large population of socially deprived groups, which are dependent on the primary sector, and high population density make the state vulnerable from a socio-economic point of view. The present study investigates climate change vulnerability at the district level in the State of Kerala relying on a purposely developed composite vulnerability index that encompasses both socio-economic and environmental factors. The Kerala coast contains the socio-economically and ecologically most vulnerable regions, as demonstrated by a composite vulnerability index.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets., (© 2015 John Wiley & Sons Ltd.)

Monitoring tropical deforestation and forest degradation is one of the central elements for the Reduced Emissions from Deforestation and Forest Degradation in developing countries (REDD+) scheme. Current arrangements for monitoring are based on remote sensing and field measurements. Since monitoring is the periodic process of assessing forest stands properties with respect to reference data, adopting the current REDD+ requirements for implementing monitoring at national levels is a challenging task. Recently, the advancement in Information and Communications Technologies (ICT) and mobile devices has enabled local communities to monitor their forest in a basic resource setting such as no or slow internet connection link, limited power supply, etc. Despite the potential, the use of mobile device system for community based monitoring (CBM) is still exceptional and faces implementation challenges. This paper presents an integrated data collection system based on mobile devices that streamlines the community-based forest monitoring data collection, transmission and visualization process. This paper also assesses the accuracy and reliability of CBM data and proposes a way to fit them into national REDD+ Monitoring, Reporting and Verification (MRV) scheme. The system performance is evaluated at Tra Bui commune, Quang Nam province, Central Vietnam, where forest carbon and change activities were tracked. The results show that the local community is able to provide data with accuracy comparable to expert measurements (index of agreement greater than 0.88), but against lower costs. Furthermore, the results confirm that communities are more effective to monitor small scale forest degradation due to subsistence fuel wood collection and selective logging, than high resolution remote sensing SPOT imagery.

One of the main tasks in analyzing pedestrian movement is to detect places where pedestrians stop, as those places usually are associated with specific human activities, and they can allow us to understand pedestrian movement behavior. Very few approaches have been proposed to detect the locations of stops in positioning data sets, and they often are based on selecting the location of candidate stops as well as potential spatial and temporal thresholds according to different application requirements. However, these approaches are not suitable for analyzing the slow movement of pedestrians where the inaccuracy of a nondifferential global positioning system commonly used for movement tracking is so significant that it can hinder the selection of adequate thresholds. In this article, we propose an exploratory statistical approach to detect patterns of movement suspension using a local indicator of spatial association (LISA) in a vector space representation. Two different positioning data sets are used to evaluate our approach in terms of exploring movement suspension patterns that can be related to different landscapes: players of an urban outdoor mobile game and visitors of a natural park. The results of both experiments show that patterns of movement suspension were located at places such as checkpoints in the game and different attractions and facilities in the park. Based on these results, we conclude that using LISA is a reliable approach for exploring movement suspension patterns that represent the places where the movement of pedestrians is temporally suspended by physical restrictions (e.g., checkpoints of a mobile game and the route choosing points of a park).

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources.

Background: Reliable access to basic services can improve a community's resilience to HIV/AIDS. Accordingly, work is being done to upgrade the physical infrastructure in affected areas, often employing a strategy of decentralised service provision. Spatial characteristics are one of the major determinants in implementing services, even in the smaller municipal areas, and good quality spatial information is needed to inform decision making processes. However, limited funds, technical infrastructure and human resource capacity result in little or no access to spatial information for crucial infrastructure development decisions at local level.This research investigated whether it would be possible to develop a GIS for basic infrastructure planning and management at local level. Given the resource constraints of the local government context, particularly in small municipalities, it was decided that open source software should be used for the prototype system., Results: The design and development of a prototype system illustrated that it is possible to develop an open source GIS system that can be used within the context of local information management. Usability tests show a high degree of usability for the system, which is important considering the heavy workload and high staff turnover that characterises local government in South Africa. Local infrastructure management stakeholders interviewed in a case study of a South African municipality see the potential for the use of GIS as a communication tool and are generally positive about the use of GIS for these purposes. They note security issues that may arise through the sharing of information, lack of skills and resource constraints as the major barriers to adoption., Conclusion: The case study shows that spatial information is an identified need at local level. Open source GIS software can be used to develop a system to provide local-level stakeholders with spatial information. However, the suitability of the technology is only a part of the system - there are wider information and management issues which need to be addressed before the implementation of a local-level GIS for infrastructure management can be successful.

Small-scale studies on long-term change in agricultural knowledge might uncover insights with broader, regional implications. This article evaluates change in farmer knowledge about crop genetic resources in highland Guatemala between 1927/37 and 2004. It concentrates on maize (Zea mays ssp. mays L.) in one Guatemalan township, Jacaltenango, an area with much ecological and maize diversity. It relies on a particular type of baseline information: lists of farmer-defined cultivars drawn up by ethnographers in the first half of the twentieth century. A questionnaire format based on two independent lists of local farmer cultivars dating from 1927 and 1937 was used to assess changes in maize diversity. Comparisons between attributes given to each cultivar in the past and in 2004 were used as a partial test of the stability of cultivar identity. In farmers' perceptions, cultivar loss was low and limited to certain cultivars adapted to the warmer environments. Crop production problems were mentioned as the main motives for change. No evidence for a loss of cultivars due to the political violence of the 1980s was found. In the lower areas many newly introduced cultivars were found, which reportedly provide solutions for the production problems the older cultivars have. The article contrasts these findings with those of an earlier study which suggested much cultivar loss due to political violence, and draws conclusions about the methodological implications.

Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.

This paper outlines a procedure that quantifies the impact of different sources of spatial variability and uncertainty on ecological risk estimates. The procedure is illustrated in a case study that estimates the risks of cadmium for a little owl (Athene noctua vidalli) living in a Dutch river flood plain along the river Rhine. A geographical information system (GIS) was used to quantify spatial variability in contaminant concentrations and habitats. It was combined with an exposure and effect model that uses Monte Carlo simulation to quantify parameter uncertainty. Spatial model uncertainty was assessed by the application of two different spatial interpolation methods (classification and kriging) and foraging ranges. The results of the case study show that parameter uncertainty is the main type of uncertainty influencing the risk estimate, and to a lesser extent spatial variability, while spatial model uncertainty was of minor importance. Compared to the deterministically calculated hazard index for the little owl (0.9), inclusion of spatial variability resulted in a median hazard index that can vary between 0.8 and 1.4. It is concluded that a single estimator for a whole flood plain may over- or underestimate risks for specific parts within the flood plain. Further research that expands the procedure presented in this paper is necessary to improve the incorporation of spatial factors in ecological risk assessment.

Multi-agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. The main goal is to explore the use of MAS to simulate spatial scenarios based on modelling multi-actor decision-making within a spatial planning process. We demonstrate MAS that consists of agents representing organizations and interest groups involved in an urban allocation problem during a land use planning process. The multi-actor based decision-making is modelled by generating beliefs and preferences of actors about the location of and relation between spatial objects. This allows each agent to confront these beliefs and preferences with it's own desires and with that of other agents. The MAS loosely resembles belief, desire and intentions architecture. Based on a case study for a hypothetical land use planning situation in a study area in the Netherlands we discuss the potential and limitations of the MAS to build models that enable spatial planners to include the 'actor factor' in their analysis and design of spatial scenarios. In addition, our experiments revealed the need for further research on the representation of spatial objects and reasoning, learning and communication about allocation problems using MAS.