Visual analysis of geospatial habitat suitability model based on inverse distance weighting with paired comparison analysis

Geospatial data analytical model is developed in this paper to model the spatial suitability of malaria outbreak in Vellore, Tamil Nadu, India. In general, Disease control strategies are only the spatial information like landscape, weather and climate, but also spatially explicit information like socioeconomic variable, population density, behavior and natural habits of the people. The spatial multi-criteria decision analysis approach combines the multi-criteria decision analysis and geographic information system (GIS) to model the spatially explicit and implicit information and to make a practical decision under different scenarios and different environment. Malaria is one of the emerging diseases worldwide; the cause of malaria is weather & climate condition of the study area. The climate condition is often called as spatially implicit information, traditional decision-making models do not use the spatially implicit information it most often uses spatially explicit information such as socio-economic, natural habits of the people. There is need to develop an integrated approach that consists of spatially implicit and explicit information. The proposed approach is used to identity an effective control strategy that prevents and control of malaria. Inverse Distance Weighting (IDW) is a type of deterministic method used in this paper to assign the weight values based on the neighborhood locations. ArcGIS software is used to develop the geospatial habitat suitability model.     

When is the discretization of a spatially distributed system good enough for control?

This paper describes a new and straightforward method for controlling spatially distributed plants based on low-order models obtained from spatial discretization techniques. A suitable level of discretization is determined by computing the sequence of ν-gaps between weighted models of successively finer spatial resolution, and bounding this by another sequence with an analytic series. It is proved that such a series forms an upper bound on the ν-gap between a weighted model in the initial sequence and the spatially distributed weighted plant. This enables the synthesis, on low-order models, of robust controllers that are guaranteed to stabilize the actual plant, a feature not shared by most model reduction methods where the gap between the high-order model and plant is often not known, and where the gap between high-order and reduced models may be too expensive to compute. Since the calculation of the current bound is based on weighted models of small state-dimension, the new method avoids the numerical problems inherent in large-scale model reduction based approaches. The ideas presented in this paper are demonstrated on a disturbance rejection problem for a 1D heat equation.     

Design of Low-Bandwidth Spatially Distributed Feedback

The paper considers a family of linear time-invariant and spatially invariant (LTSI) systems that are both distributed and localized. The spatial responses of the distributed plant are localized in spatial neighborhoods of each location. The feedback computations are also distributed and the information flow is localized in a spatial neighborhood of each location. The feedback is aimed at controlling spatial distributions of variables in the systems with a relatively low bandwidth in the time direction. Such systems have many important applications including industrial processes, imaging systems, signal and image processing, and others. We describe a new method for designing (tuning) a certain family of low-bandwidth controllers for such plants. We consider LTSI controllers with a fixed structure, which is a PID or a similar low-bandwidth feedback in time and local in spatial coordinates. Two spatial feedback filters, symmetric and with finite spatial response, modify the local PID control signal by mixing in the error and control signals at nearby nodes. These two filters provide loopshaping and regularization of the spatial feedback loop. Like an ordinary PID controller, this controller structure is simple, but provides adequate performance in many practical settings. We cast a variety of specifications on the steady-state spatial response of the controller and its time response as a set of linear inequalities on the design variables, and so can carry out the design of the spatial filters using linear programming. The method handles steady-state limits on actuator signals, error signals, and several constraints related to robustness to plant and controller variation. The method allows handling the effects of boundary conditions and guaranteed closed-loop spatial or time decay. It does appear to work very well for low-bandwidth controllers, and so is applicable in a variety of practical situations.      

Influence-based model decomposition for reasoning about spatially distributed physical systems

Many important science and engineering applications, such as regulating the temperature distribution over a semiconductor wafer and controlling the noise from a photocopy machine, require interpreting distributed data and designing decentralized controllers for spatially distributed systems. Developing effective computational techniques for representing and reasoning about these systems, which are usually modeled with partial differential equations (PDEs), is one of the major challenge problems for qualitative and spatial reasoning research.

This paper introduces a novel approach to decentralized control design, influence-based model decomposition, and applies it in the context of thermal regulation. Influence-based model decomposition uses a decentralized model, called an influence graph, as a key data abstraction representing influences of controls on distributed physical fields. It serves as the basis for novel algorithms for control placement and parameter design for distributed systems with large numbers of coupled variables. These algorithms exploit physical knowledge of locality, linear superposability, and continuity, encapsulated in influence graphs representing dependencies of field nodes on control nodes. The control placement design algorithms utilize influence graphs to decompose a problem domain so as to decouple the resulting regions. The decentralized control parameter optimization algorithms utilize influence graphs to efficiently evaluate thermal fields and to explicitly trade off computation, communication, and control quality. By leveraging the physical knowledge encapsulated in influence graphs, these control design algorithms are more efficient than standard techniques, and produce designs explainable in terms of problem structures.     

Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau,China

Knowing the spatial relationships between the normalized difference vegetation index (NDVI) and environmental variables is of great importance for monitoring rocky desertification. This article investigated the spatially non-stationary relationships between NDVI and environmental factors using geographically weighted regression (GWR) at multi-scales. The spatial scale-dependency of the relationships between NDVI and environmental factors was identified by scaling the bandwidth of the GWR model, and the appropriate bandwidth of the GWR model for each variable was determined. All GWR models represented significant improvements of model performance over their corresponding ordinary least squares (OLS) models. GWR models also successfully reduced the spatial autocorrelations of residuals. The spatial relationships between NDVI and environmental factors significantly varied over space, and clear spatial patterns of slope parameters and local coefficient of determination (R ²) were found from the results of the GWR models. The study revealed detailed site information on the different roles of related factors in different parts of the study area, and thus improved the model ability to explain the local situation of NDVI.     

分布式空间信息的对等协同计算机制研究*

海量空间信息的处理需要分布式协同工作的GIS平台支持。为解决空间数据源的异构和分布式网络中的计算能力共享问题,设计了分布式空间信息的协同计算模型,分析了分布式空间信息协同计算具备的基本特征;从空间数据分布存储模型、空间数据分布式计算协同和分布式空间数据并行索引等方面讨论分布式空间信息的协同计算技术体系,并提出现阶段可行的实现机制。分布式对等协同计算机制避免了集中式执行引擎带来的网络拥塞和单点失效问题,提高了海量空间信息资源和计算资源协作的可靠性和可用性。     

Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.     

Spatial associative classification: propositional vs structural approach

In Spatial Data Mining, spatial dimension adds a substantial complexity to the data mining task. First, spatial objects are characterized by a geometrical representation and relative positioning with respect to a reference system, which implicitly define both spatial relationships and properties. Second, spatial phenomena are characterized by autocorrelation, i.e., observations of spatially distributed random variables are not location-independent. Third, spatial objects can be considered at different levels of abstraction (or granularity). The recently proposed SPADA algorithm deals with all these sources of complexity, but it offers a solution for the task of spatial association rules discovery. In this paper the problem of mining spatial classifiers is faced by building an associative classification framework on SPADA. We consider two alternative solutions for associative classification: a propositional and a structural method. In the former, SPADA obtains a propositional representation of training data even in spatial domains which are inherently non-propositional, thus allowing the application of traditional data mining algorithms. In the latter, the Bayesian framework is extended following a multi-relational data mining approach in order to cope with spatial classification tasks. Both methods are evaluated and compared on two real-world spatial datasets and results provide several empirical insights on them.     

Measurement of habitat predictor variables for organism-habitat models using remote sensing and image segmentation

Robust predictive models of the effects of habitat change on species abundance over large geographical areas are a fundamental gap in our understanding of population distributions, yet are urgently required by conservation practitioners. Predictive models based on underpinning relationships between environmental predictors and the individual organism are likely to require measurement of spatially fine-grained predictor variables. Further, models must show spatial generality if they are to be used to predict the consequences of habitat change over large geographical areas. Remote sensing techniques using airborne scanning laser altimetry (LiDAR) and high resolution multi-spectral imagery allow spatially fine-grained predictor variables to be measured over large geographical areas and thus facilitate testing of the spatial generality of organism-habitat models. These techniques are considered using the skylark as an example species. A range image segmentation system for LiDAR data is described which allows measurement of skylark habitat predictor variables such as within-field vegetation height, boundary height and shape for individual fields within the LiDAR image. Additional variables such as field vegetation type and fractional vegetation ground cover may be obtained from co-registered multi-spectral data. These techniques could have wide application in testing the generality of relationships between populations and habitats, and in ecological monitoring of change in habitat structures and the associated effects on wildlife, over large geographical areas.     

A software system for assessing the spatially distributed ecological risk posed by oil shipping

A maritime accident involving an oil tanker may lead to large scale mortality or reductions in populations of coastal species due to oil. The ecological value at stake is the biota on the coast, which are neither uniformly nor randomly distributed. We used an existing oil spill simulation model, an observation database of threatened species, and a valuation method and developed a software system for assessing the spatially distributed ecological risk posed by oil shipping. The approach links a tanker accident model to a set of oil spill simulations and further to a spatial ecological value data set. The tanker accident model is a Bayesian network and thus we present a case of using a Bayesian network in geographic analysis. A case in the Gulf of Finland is used for illustration of the methodology. The method requires and builds on an extensive data collection and generation effort and modeling. The main difference of our work to earlier works on using a Bayesian network in geospatial setting is that in our case the Bayesian network was used to compute the probabilities of spatial scenarios directly in a global sense while in earlier works Bayesian networks have been used for each location separately to obtain global results. The result was a software system that was used by a distributed research team.     

A predictive modelling technique for human population distribution and abundance estimation using remote-sensing and geospatial data in a rural mountainous area in Kenya

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km² (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km²; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D ², adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands.     

On the impact of network latency on distributed systems design

Research in distributed database systems to date has assumed a “variable cost” model of network response time. However, network response time has two components: transmission time (variable with message size) and latency (fixed). This research improves on existing models by incorporating a “fixed plus variable cost” model of the network response time. In this research, we: (1) develop a distributed database design approach that incorporates a “fixed plus variable cost”, network response time function; (2) run a set of experiments to create designs using this model, and (3) evaluate the impact the new model had on the design in various types of networks. This revised version was published online in July 2006 with corrections to the Cover Date.     

Gene selection using independent variable group analysis for tumor classification

Microarrays are capable of detecting the expression levels of thousands of genes simultaneously. So, gene expression data from DNA microarray are characterized by many measured variables (genes) on only a few samples. One important application of gene expression data is to classify the samples. In statistical terms, the very large number of predictors or variables compared to small number of samples makes most of classical “class prediction” methods unemployable. Generally, this problem can be avoided by selecting only the relevant features or extracting new features containing the maximal information about the class label from the original data. In this paper, a new method for gene selection based on independent variable group analysis is proposed. In this method, we first used t-statistics method to select a part of genes from the original data. Then, we selected the key genes from the selected genes for tumor classification using IVGA. Finally, we used SVM to classify tumors based on the key genes selected using IVGA. To validate the efficiency, the proposed method is applied to classify three different DNA microarray data sets. The prediction results show that our method is efficient and feasible.     

一种改进的三级倒立摆变论域模糊控制器设计

在传统变论域模糊控制系统中, 论域随着输入的变化实时改变, 论域的反复调整降低了控制的实时性, 同时伸缩因子的函数结构和参数也不易确定. 基于上述问题本文设计了基于改进型变论域算法的三级倒立摆模糊控制器: 首先提出了相对变论域控制思想, 然后采用模糊逻辑推理器构造了伸缩因子, 实时调整输入变量, 从而相对性地改变论域大小, 避免了传统伸缩因子的函数结构和参数不易确定的问题, 并根据系统闭环响应曲线设计了控制 器输出调整因子. 最后采用极点配置方法对状态变量进行综合, 避免了规则爆炸问题. 三级倒立摆的仿真结果表明了该方法具有较好的控制效果.     

空间数据网络处理服务模型及关键技术

面向空间数据处理的服务描述、部署、发现、调用过程是空间数据服务化处理的关键问题,直接关系到空间分析与相关数据处理计算的实现方式和执行效率。在标准网络服务模式之上,参照OGC规范设计空间数据网络过程处理服务的实现模型。并在空间数据分析和网络处理服务模型基础上,对网络服务的资源结构、服务调用模式、空间分析函数、数据处理流程等部分给出设计和定义。并以空间缓冲区分析算法为实例,实现过程处理服务模型实例,并给出分布式网络环境下空间数据处理服务的发布、调用与计算模式的完整实现方法。     

The utility of built environment geospatial data for high-resolution dasymetric global population modeling

Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built environment (e.g. built-up and building footprint data). Accordingly, these gridded population data are increasingly dependent on a single ancillary data set to inform the distribution of populations across space. Our study tests several combinations of binary masking variables (land areas, all building footprints, residential building footprints) and density variables (building footprint areas, building volumes) derived from characteristics of the built environment at 20× and 8000× downscaling using a flexible equation for high-resolution global dasymetric population modeling. The assessment is applied in New York City, where large spatial heterogeneities exist across confined geographic areas. Results confirm that the performance of the model generally improves as: (i) the binary masking variable becomes increasingly limiting; and, (ii) the density variable becomes more pronounced. However, application requires careful consideration due to their propensity to amplify both positive results and errors.     

分布式空间信息的对等协同混合发现模型*

海量空间信息的处理需要分布式协同工作的GIS平台的支持,为了解决经典的分散式结构化的分布式哈希表逻辑网络结构增加的延时和在构建哈希表的过程中逻辑覆盖网络往往和物理网络不一致的问题,提出一种分布式空间信息的对等协同混合发现模型。基于空间资源发现代理节点和普通邻居节点,该模型实现了集中式的全局空间资源发现模型与分散式结构化的分布式哈希表模型之间的自动切换,能够自适应地调整空间资源的逻辑网络结构以提供更好的性能。基于节点交换机制,设计了构建路由表和降低延时的算法,通过发现有利于覆盖网络和物理网络匹配的节点交换来     

Supporting device discovery and spontaneous interaction with spatial references

The RELATE interaction model is designed to support spontaneous interaction of mobile users with devices and services in their environment. The model is based on spatial references that capture the spatial relationship of a user’s device with other co-located devices. Spatial references are obtained by relative position sensing and integrated in the mobile user interface to spatially visualize the arrangement of discovered devices, and to provide direct access for interaction across devices. In this paper we discuss two prototype systems demonstrating the utility of the model in collaborative and mobile settings, and present a study on usability of spatial list and map representations for device selection.     

Modelling the impact of socioeconomic structure on spatial health outcomes

A factor analytic model is proposed for the impact of spatially defined latent social constructs on area health outcomes (e.g. mortality or hospitalisation counts). The model has two components or sub-models. The first component is a social indicator measurement model using socioeconomic variables (e.g. from population censuses) as indicators of latent social constructs. The other sub-model considers variations in spatial health outcomes in terms both of the latent social constructs and of residual common factors — the latter have only the health variation component as their measurement model. The two sets of latent variables can be mutually correlated and latent scores can be correlated over areas, though the extent of the spatial dependence in the scores on any particular latent variable is determined by the data. A case study application considers the impact of two latent social constructs (denoted as social deprivation and social fragmentation) on four types of psychiatric hospitalisation in 33 local authorities in London, England.     

Spatial properties of high-mode bifurcations of distributed logistic equations

The local dynamics of the solution of spatially distributed logistic equations are studied. It is shown that critical cases in the problem of equilibrium stability have infinite dimension. New bifurcation phenomena that only arise in the case of a two-dimensional spatial variable are revealed.