Spatial dependency of cholera prevalence on potential cholera reservoirs in an urban area,Kumasi, Ghana

Cholera has been a public health burden in Ghana since the early 1970s. Between 1999 and 2005, a total of 25,636 cases and 620 deaths were officially reported to the WHO. In one of the worst affected urban cities, fecal contamination of surface water is extremely high, and the disease is reported to be prevalent among inhabitants living in close proximity to surface water bodies. Surface runoff from dump sites is a major source of fecal and bacterial contamination of rivers and streams in the study area. This study aims to determine (a) the impacts of surface water contamination on cholera infection and (b) detect and map arbitrary shaped clusters of cholera. A Geographic Information System (GIS) based spatial analysis is used to delineate potential reservoirs of the cholera vibrios; possibly contaminated by surface runoff from open space refuse dumps. Statistical modeling using OLS model reveals a significant negative association between (a) cholera prevalence and proximity to all the potential cholera reservoirs (R² = 0.18, p < 0.001) and (b) cholera prevalence and proximity to upstream potential cholera reservoirs (R² = 0.25, p < 0.001). The inclusion of spatial autoregressive coefficients in the OLS model reveals the dependency of the spatial distribution of cholera prevalence on the spatial neighbors of the communities. A flexible scan statistic identifies a most likely cluster with a higher relative risk (RR = 2.04, p < 0.01) compared with the cluster detected by circular scan statistic (RR = 1.60, p < 0.01). We conclude that surface water pollution through runoff from waste dump sites play a significant role in cholera infection.     

Predicting the distribution of C3 (Festuca spp.) grass species using topographic variables and binary logistic regression model

The spatial distribution of different C3 and C4 grass species in tropical montane areas is commonly influenced by a number of factors that include site-specific topography. Hence, the distribution of these grasses across topographic gradients can vary significantly. In this study, we investigate the influence of topographic factors (elevation, slope and aspect) on the spatial distribution of Festuca grass species in a commonage area, comprising agro-biodiversity conservation land use. Integration of the topographic variables using GIS and binary logistic regression (LR) modelling showed that C3, Festuca grass species distribution can be predicted or mapped with an accuracy of 80% in the landscape under study. The study contributes to understanding the spatial distribution of C3 grass species and provides valuable information for designing and optimizing rangeland conservation in the subtropical montane landscapes.     

新疆地区鸟类和哺乳动物丰富度与环境因子的空间格局与关系

干旱区生态系统极易受到气候及土地利用变化的影响,其生物多样性格局及其形成机制是重要的生态学问题。基于新疆地区鸟类及哺乳动物物种多样性数据,结合气候、地形和长时间序列的植被遥感参数产品FAPAR数据等,主要在不同的土地利用类型及海拔带上采用单因子相关分析方法探讨了物种丰富度格局的形成机制。总体来说,不同生境类型中,植被遥感参数因子(DHI、NDVI等)与两种类群物种丰富度分布的相关性强于与气候因子(温度、降水)的相关性。具体而言,植被遥感参数因子中,基于FAPAR的生境指数因子与丰富度的相关性大于基于植被指数的因子(DHI_cumNDVI_cumEVI_cum);气候因子中,在草地生境或者较低的海拔上,年均降水因子对于丰富度分布的解释力强于年均温度因子。这表明在新疆地区,影响鸟类与哺乳类动物物种丰富度分布的主导理论是生境异质性假说与环境稳定性假说,其解释力在多种生境内均强于生产力与环境热量。     

面向开采扰动的离子型稀土矿区地表温度降尺度方法

离子型稀土的开采活动会导致矿区地表极其剧烈的生态扰动,并且会造成当地的生态环境问题,而矿区地表热环境分异变化能较好地反映矿区的生态扰动特点,是一种辨识地表生态扰动的重要参数.离子型稀土矿区存在矿点分散且单个矿点面积较小的特征,因此获取实用性强且空间分辨率更高的地表温度数据对稀土矿区生态环境的监测具有重要价值.构建了一种...     

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water.     

Spatial modelling of sprinkler irrigation suitability in a Central Brazilian Cerrado region

Sprinkler irrigation, an agricultural production system that is causing increasing conflict among water resource users, is expanding quickly in the Central Western Cerrado regions of Brazil. To subsidize watershed management and concession of water rights, GIS-based spatial modelling was applied to spatially predict relative likelihood of the installation of centre sprinkler irrigation systems. Interpretation of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper imagery was conducted to map spatial distribution of centre-pivot sprinkler systems. Multi-source data layers on environmental conditions and infrastructure were elaborated to test their predictive power in an Ecological Niche Factor Analysis, a spatial modelling technique for presence-only data. Underpinned by an exploratory analysis of spatial autocorrelation of irrigation systems, suitability predictions were found to be accurate on landscape scale and improved when the model includes underlying ecogeographical factors (EGV) such as farming suitability, soil groupings and distance to the hydrographic network and a density layer of existing irrigations.     

The pyrogeography of sub-Saharan Africa: a study of the spatial non-stationarity of fire–environment relationships using GWR

This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well (R ² = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires.     

Fractal characterization of the spatial distribution of geological point processes

Geological point processes can be used to model point patterns occurring frequently in a wide variety of geoscience fields, including the study of mineral deposits, oil producing wells, earthquakes, and landslides. Characterization of the spatial distribution of GPP has implications for understanding the properties of the underlying geological processes and events. Three examples of GPP dealing with (1) metallic mineral deposits, (2) oil producing wells, and (3) aftershocks of the Wenchuan earthquake (on 12 May 2008, magnitude 8.0) are presented to illustrate that (1) the spatial distribution of geological point processes generally shows clustering implying rejection of the Poisson model because L(r) > L_Pois(r); (2) the clustering statistics of the underlying geological processes are fractal; and (3) the size distribution of geological point processes is scale invariant. These results indicate existence of a fundamental law concerning the fractal nature of the point distributions generated by geological point processes.     

Using GIS and Dispersion Modelling Tools to Assess the Effect of the Environment on Health

A feasibility study in progress to integrate health and air quality information is presented. The methods of using integrated GIS and air quality dispersion modelling tools to assess the effect of the environment on health are discussed. The main aim is to model human personal exposure to air pollutants and compare the predictions to respiratory health data for asthmatics in a health telematics project. The MEDICATE project develops and tests the feasibility of using a health telematic system for asthmatics. Key to this is the determination of the real-time health (lung function) response to the ambient environment. For this, air quality information is related to respiratory measurements by modelling personal exposure through a GIS. The methods of integrating environmental modelling and assessment tools (GIS) in this case are examined. ESRI's ArcView is used to locate and compile environmental information about the patients' locations and lifestyles in the study areas (London and Barcelona). A dispersion modelling extension to ArcView, ADMS Urban, is used to interrogate the spatial environmental databases (e.g. emission inventories) to model air pollution concentrations. Patients' personal exposure is modelled by time-weighted estimates of their exposure to ambient air quality at each defined location. Lung function data can be compared on a time-wise basis with these air quality indicators to see if there is a relationship on an hourly, daily or lagged-day basis. The aim of this paper is to propose a methodology and discuss the integration and practicalities of using air quality assessment tools (inventories, dispersion modelling and a GIS) in assessing the impacts of the environment on human health.     

Control of spatial discretisation in coastal oil spill modelling

Spatial discretisation plays an important role in many numerical environmental models. This paper studies the control of spatial discretisation in coastal oil spill modelling with a view to assure the quality of modelling outputs for given spatial data inputs. Spatial data analysis techniques are effective for investigating and improving the spatial discretisation in different phases of the modelling. Proposed methods are implemented and tested with experimental models. A new “Automatic Search” method based on GIS zone design principles is shown to significantly improve discretisation of bathymetric data and hydrodynamic modelling outputs. The concepts and methods developed in the study are expected to have general relevance for a range of applications in numerical environmental modelling.     

基于GIS的采煤沉陷区地质环境数据库建设研究

文章梳理了采煤沉陷区地质环境调查基础数据类型和空间数据类型,依据我国矿山地质环境调查评价的行业标准和规范,建立了集成数据管理、服务、交换体系的综合数据库,实现了基于GIS的综合业务数据集成、管理与服务。结果表明:GIS技术可以实现采煤沉陷区环境地质调查数据库建设,为采煤沉陷区的土地治理和生态恢复提供决策支持。     

GIS-based landslide susceptibility model considering effective contributing area for drainage time

This study employed GIS modelling to ascertain landslide susceptibility on Mt. Umyeon, south of Seoul, South Korea. In this study, an effective contributing area (ECA) for certain drainage time was purposed as a temporal causative factor and then used for modelling in combination with spatial causative factors such as elevation, slope, plan curvature, drainage proximity, forest type, soil type and geology. Landslide inventory map of 163 landslide locations was prepared using aerial photographic interpretation and field verifications after that digitized using GIS environment in 1:5000 scale. A presence-only-based maximum entropy model was used to establish and analyse the relationship between landslides and causative factors. Before final modelling, a jackknife test was performed to measure the variable contributions, which showed that the slope was the most significant spatial causative factor, and ECA with a drainage time of 12 h was the most significant temporal causative factor. The performances of the final models, with and without significant ECA, were assessed by plotting a receiver operating characteristic curve to be 75.5 and 81.2%, respectively.     

基于梯度结构相似度的矿区土壤湿度空间分析

基于中国蒙、陕、晋、三省区的神东矿区2000-2015年成像光谱仪数据,双抛物线型归一化植被指数（normalized difference vegetation index,NDVI）和地表辐射温度（land surface temperature,T_s）（记为NDVI-T_s）特征空间的温度植被干旱指数法计量地表土壤湿度,采用梯度结构相似度法定量分析研究区土壤湿度的时空分布特征。结果表明:神东矿区土壤湿度变化具有明显时空分布异质性,空间上,矿区土壤湿度表现出从西北部向东南部逐渐增加的规律,干旱区域由2000年的96.03%下降到2015年的59.59%;矿区60.05%的区域的土壤湿度发生了突变,其中49.87%区域地表植被覆盖得到明显改善,土壤湿度得到明显提高;35.18%的区域的土壤湿度发生了变化,其中28.13%区域地表植被覆盖有所改善,土壤湿度有所增加;仅有4.77%的区域的土壤湿度没有发生改变。进一步分析表明,地表土壤湿度的时空分布特征受区域地貌类型和下垫面覆盖影响较大。     

Spatial Preference Modelling for equitable infrastructure provision: an application of Sen’s Capability Approach

To determine whether the inclusion of spatial neighbourhood comparison factors in Preference Modelling allows spatial decision support systems (SDSSs) to better address spatial equity, we introduce Spatial Preference Modelling (SPM). To evaluate the effectiveness of this model in addressing equity, various standardisation functions in both Non-Spatial Preference Modelling and SPM are compared. The evaluation involves applying the model to a resource location-allocation problem for transport infrastructure in the Special Province of Yogyakarta in Indonesia. We apply Amartya Sen’s Capability Approach to define opportunity to mobility as a non-income indicator. Using the extended Moran’s I interpretation for spatial equity, we evaluate the distribution output regarding, first, ‘the spatial distribution patterns of priority targeting for allocation’ (SPT) and, second, ‘the effect of new distribution patterns after location-allocation’ (ELA). The Moran’s I index of the initial map and its comparison with six patterns for SPT as well as ELA consistently indicates that the SPM is more effective for addressing spatial equity. We conclude that the inclusion of spatial neighbourhood comparison factors in Preference Modelling improves the capability of SDSS to address spatial equity. This study thus proposes a new formal method for SDSS with specific attention on resource location-allocation to address spatial equity.     

An integrated environment for developing knowledge-based spatial decision support systems

Knowledge-based spatial decision support systems (KBSDSS) incorporate heuristic reasoning, and analytical and spatial modelling capabilities to support decision making in resource and environmental management. They are able to provide the assistance for both the structuring and formulation of the spatial problem, and the design and execution of a solution process by automatically integrating different types of models and data. This paper presents a KBSDSS development environment, which connects a GIS tool (ARC/INFO) with an expert system tool (CLIPS) and a hypertext diagramming tool (HARDY). ARC/INFO provides capabilities for spatial data handling. CLIPS is used to develop knowledge bases and heuristic reasoning. HARDY is used to represent spatial problems in terms of diagrams that can then be translated into a CLIPS knowledge base for evaluation. The development environment allows users to structure their problems and develop models for particular requirements and domains.     

Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data

The accurate mapping of urban housing prices at a fine scale is essential to policymaking and urban studies, such as adjusting economic factors and determining reasonable levels of residential subsidies. Previous studies focus mainly on housing price analysis at a macro scale, without fine‐scale study due to a lack of available data and effective models. By integrating a convolutional neural network for united mining (UMCNN) and random forest (RF), this study proposes an effective deep‐learning‐based framework for fusing multi‐source geospatial data, including high spatial resolution (HSR) remotely sensed imagery and several types of social media data, and maps urban housing prices at a very fine scale. With the collected housing price data from China's biggest online real estate market, we produced the spatial distribution of housing prices at a spatial resolution of 5 m in Shenzhen, China. By comparing with eight other multi‐source data mining techniques, the UMCNN obtained the highest housing price simulation accuracy (Pearson R = 0.922, OA = 85.82%). The results also demonstrated a complex spatial heterogeneity inside Shenzhen's housing price distribution. In future studies, we will work continuously on housing price policymaking and residential issues by including additional sources of spatial data.     

Predictive fire occurrence modelling to improve burned area estimation at a regional scale: A case study in East Caprivi,Namibia

Fires threaten human lives, property and natural resources in Southern African savannas. Due to warming climate, fire occurrence may increase and fires become more intense. It is crucial, therefore, to understand the complexity of spatiotemporal and probabilistic characteristics of fires. This study scrutinizes spatiotemporal characteristics of fires and the role played by abiotic, biotic and anthropogenic factors for fire probability modelling in a semiarid Southern African savanna environment. The MODIS fire products: fire hot spots (MOD14A2 and MYD14A2) and burned area product MODIS (MCD45A1), and GIS derived data were used in analysis. Fire hot spots occurrence was first analysed, and spatial autocorrelation for fires investigated, using Moran's I correlograms. Fire probability models were created using generalized linear models (GLMs). Separate models were produced for abiotic, biotic, anthropogenic and combined factors and an autocovariate variable was tested for model improvement. The hierarchical partitioning method was used to determine independent effects of explanatory variables. The discriminating ability of models was evaluated using area under the curve (AUC) from the receiver operating characteristic (ROC) plot. The results showed that 19.2–24.4% of East Caprivi burned when detected using MODIS hot spots fire data and these fires were strongly spatially autocorrelated. Therefore, the autocovariate variable significantly improved fire probability models when added to them. For autologistic models, i.e. models accounting for spatial autocorrelation, discrimination was good to excellent (AUC 0.858–0.942). For models not counting spatial autocorrelation, prediction success was poor to moderate (AUC 0.542–0.745). The results of this study clearly showed that spatial autocorrelation has to be taken in to account in the fire probability model building process when using remotely sensed and GIS derived data. This study also showed that fire probability models accounting for spatial autocorrelation proved to be superior in regional scale burned area estimation when compared with MODIS burned area product (MCD45A1).     

Detecting patterns of climate change in long-term forecasts of marine environmental parameters

ABSTRACT

Forecasting environmental parameters in the distant future requires complex modelling and large computational resources. Due to the sensitivity and complexity of forecast models, long-term parameter forecasts (e.g. up to 2100) are uncommon and only produced by a few organisations, in heterogeneous formats and based on different assumptions of greenhouse gases emissions. However, data mining techniques can be used to coerce the data to a uniform time and spatial representation, which facilitates their use in many applications. In this paper, streams of big data coming from AquaMaps and NASA collections of 126 long-term forecasts of nine types of environmental parameters are processed through a cloud computing platform in order to (i) standardise and harmonise the data representations, (ii) produce intermediate scenarios and new informative parameters, and (iii) align all sets on a common time and spatial resolution. Time series cross-correlation applied to these aligned datasets reveals patterns of climate change and similarities between parameter trends in 10 marine areas. Our results highlight that (i) the Mediterranean Sea may have a standalone ‘response’ to climate change with respect to other areas, (ii) the Poles are most representative of global forecasted change, and (iii) the trends are generally alarming for most oceans.     

The use of motor-glider in topoclimatic studies

This paper reports on the advantages and disadvantages of motor-glider use in studying topoclimates. Despite the widespread use of images taken from low-altitude flying platforms (planes, helicopters, UAVs), the use of a motor-glider for imagery collection has not been reported in environmental studies. In presented study, the low-altitude remote sensing techniques were used to increase the spatial resolution of thermal maps derived from Landsat ETM+ thermal bands. Thermal images from motor-glider were taken by a thermovision camera. At the local scale, land surface temperature (LST) is one of the factors influencing topoclimatic diversity hence, by analysing LST distribution one can determine topoclimatic variability. Topoclimate has been the subject of previous studies, however, they have not used thermal remote sensing in the research process but instead relied on ground measurement network. The presented research contributes to better understanding of the thermal environment of the Earth by employing an innovative data collection method suitable for relatively large areas under specific weather conditions. The data collection with motor glider offers good spatial resolution of less than 1 m and facilitates the compilation of good quality LST maps. The paper discusses the influence of spatial resolution on LST variability and demonstrates gain in information granularity resulting from sub-meter resolution of collected data.     

黄河流域乡村社会经济与生态环境耦合协调测度及影响因素分析

基于黄河流域2008、2010、2012、2014年136个区县面板数据,本文运用熵权TOPSIS分析法、耦合协调度模型和地理探测器模型对其耦合协调水平和影响因素进行了分析。结果表明:①研究期内黄河流域社会经济发展和生态环境质量耦合协调度变化趋势呈“U”形曲线,总体处于高度耦合低级协调阶段。区域间差异变化趋势呈倒“U”形曲线。从相对发展度模型来看,2010—2014年乡村生态环境质量相对滞后。②空间格局上存在显著的空间分异特征,耦合协调度呈“上游>中游>下游”的分布格局。③自然环境因素是影响耦合协调度空间分异的主导因素,其次是社会经济因素。其中,乡村人口密度、PM_2.5年均浓度、人均耕地面积、地形起伏度、高程和年均气温是主要因子,并且两个因子的交互作用明显大于单一因子的影响。基于此,建议黄河流域以协调发展理念为指引,重点提高生态环境的保护和修复,采用差异化、特色化的发展策略,以期早日实现黄河流域生态保护和高质量发展。