Geospatial Health, Volume 8, Number 2, May 2014, Pages 429-435
By Jia-Cheng Zhang, Wen-Dong Liu, Qi Liang, Jian-Li Hu, Jessie Norris, Ying Wu, Chang-Jun Bao, Fen-Yang Tang, Peng Huang, Yang Zhao, Rong-Bin Yu, Ming-Hao Zhou, Hong-Bing Shen, Feng Chen, and Zhi-Hang Peng
“Influenza poses a constant, heavy burden on society. Recent research has focused on ecological factors associated with influenza incidence and has also studied influenza with respect to its geographic spread at different scales. This research explores the temporal and spatial parameters of influenza and identifies factors influencing its transmission.
Spatial clusters of annual incidence of influenza (hotspots) in Jiangsu province, P.R. China, for the years 2004 (a), 2006 (b), 2009 (c) and 2011 (d).
“A spatial autocorrelation analysis, a spatial-temporal cluster analysis and a spatial regression analysis of influenza rates, carried out in Jiangsu province from 2004 to 2011, found that influenza rates to be spatially dependent in 2004, 2005, 2006 and 2008. South-western districts consistently revealed hotspots of high-incidence influenza. The regression analysis indicates that railways, rivers and lakes are important predictive environmental variables for influenza risk. A better understanding of the epidemic pattern and ecological factors associated with pandemic influenza should benefit public health officials with respect to prevention and controlling measures during future epidemics. ”
GIScience & Remote Sensing, Volume 51, Issue 2, 2014 — Special Issue: Coastal Remote Sensing
By Yong Hoon Kim, Jungho Im, Ho Kyung Ha, Jong-Kuk Choi, and Sunghyun Ha
“Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m3 (40.7%) for chl-a, and calibration R2 of 0.98 and CV RMSE of 11.42 g/m3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea.”
Transactions in GIS, Volume 18, Issue 3, pages 449–463, June 2014
By Letícia de Barros Viana Hissa and Britaldo Silveira Soares Filho
“Recently, the increasing demand for biofuels triggered a new phase for the sugar-alcohol sector. In Brazil, as well as in other tropical countries, this process raised worries regarding the possible direct and indirect effects of the crop’s expansion on the conversion of native vegetation coverings. Therefore, the modeling of spatial-economic surfaces, representing the potential rent variation in its spatial component, for economic activities, may be a useful tool in the decision-making process. Hence, here we propose and present the results of a combined framework composed of two modules using the modeling platform Dinamica EGO.
Sugarcane crops estimated rentability for the harvest year 2005–2006 according to real (S1) and maximum (S2) rentability scenarios.
“The first module simulates sugarcane’s growth, calculating the daily response of the crop to environmental conditions during the stages of the plant’s development. The second module estimates rents for sugarcane cultivation in Brazil, identifying areas where this activity would bring higher economic return, looking at simulated productivity, production costs and selling prices in a way that is spatially explicit for Brazil. Two different scenarios for production costs were tested, and results ranged from negative values to maxima of R$/ha 929 and R$/ha 1176 for standard and efficient costs of production, respectively. The model successfully indicated non-profitable and profitable areas, and regions where high expected economic return overlaps endangered ecosystems.”
Transactions in GIS, Volume 18, Issue 3, pages 436–448, June 2014
By Jun Luo
“Analysis of spatial access to healthcare services is critical for effective health resource planning. Gravity-based spatial access models have been widely used to estimate spatial access to healthcare services. Among them, the floating catchment area (FCA) methods have been proved to be informative and helpful to the designation of Health Professional Shortage Areas (HPSAs). This article integrates the Huff Model with the FCA method to articulate population selection on services. Through the proposed approach, population demand on healthcare services is adjusted by a Huff Model-based selection probability that reflects the impacts of both distance impedance and service site capacity.
Spatial patterns of the census tracts’ spatial access to healthcare services for each distance impedance coefficient. Note: β is the distance impedance coefficient
“The new approach moderates the over- or under-estimating of population demand that occurred with previous methods. Furthermore, the method uses a continuous distance impedance weight function instead of the arbitrarily defined subzones of previous studies. A case study of spatial access to primary care in Springfield, MO, showed that the proposed method can effectively moderate the population demand on service sites and therefore can generate more reliable spatial access measures.”
Transactions in GIS, Volume 18, Issue 3, pages 351–369, June 2014
By Anastasia Petrenko, Anton Sizo, Winchel Qian, A. Dylan Knowles, Amin Tavassolian, Kevin Stanley, and Scott Bell
“The popularization of tracking devices, such as GPS, accelerometers and smartphones, have made it possible to detect, record, and analyze new patterns of human movement and behavior. However, employing GPS alone for indoor localization is not always possible due to the system’s inability to determine location inside buildings or in places of signal occlusion. In this context, the application of local wireless networks for determining position is a promising alternative solution, although they still suffer from a number of limitations due to energy and IT-resources. Our research outlines the potential for employing indoor wireless network positioning and sensor-based systems to improve the collection of tracking data indoors.
3D model of campus that represents the building floors where the participants mainly spent their time. Colors close to red correspond to the locations with a high number of daily duty cycles with data, blue to those with a low number.
“By applying various methods of GIScience we developed a methodology that can be applicable for diverse human indoor mobility analysis. To show the advantage of the proposed method, we present the result of an experiment that included mobility analysis of 37 participants. We tracked their movements on a university campus over the course of 41 days and demonstrated that their movement behavior can be successfully studied with our proposed method.”