Statistics for Spatial Data.

The reconnaissance drought index (RDI) for the Setsoto municipality of the Free State province in South Africa was calculated for the period between 1985 and 2019 at 3 month (October–December), 6 month (October–March), and 12 month (October–September) intervals. Rainfall and minimum and maximum temperature data from four weather stations (Clocolan, Ficksburg, Marquard, and Senekal) were used for this study to characterize drought using “DrinC” software together with the Mann Kendall test with Sen’s slope to detect drought trends and the rate of change. Extreme, severe, and moderate droughts were recorded for all the stations, with RDIs ranging from −3.6 to −1.0 at different temporal scales. The years 1991, 1994, 2006, 2011, and 2015 were highlighted using the RDI 3, 6, and 12 month calculations. Results showed that the yield decreased either in the year of the drought or in the subsequent year, due to the exact timing of the low-rainfall events in the season and soil moisture storage. Yields were low, on average 2.5 tons ha−1 year−1, with high variability. Optimal growing conditions are essential in the early part of the season, October–December, for maximizing yield; if droughts are experienced at this time then the yield is more greatly impacted than if the droughts occur later in the season. Spatial analysis shows a large variability of drought patterns across the Municipality, over the years, with the 3 month RDI values giving a more detailed picture of this variability than the 6 and 12 month RDI values.

https://www.mdpi.com/2073-4441/12/11/2993/pdf

Drought Characterization and Trend Detection Using the Reconnaissance Drought Index for Setsoto Municipality of the Free State Province of South Africa and the Impact on Maize Yield

espanolEl presente articulo permite optimizar el costo por despliegue de los puntos de agregacion de datos universales (UDAP) para la infraestructura avanzada de medicion inteligente (AMI) considerando cobertura y capacidad. Se presenta un modelo de enrutamiento entre UDAPs basado en minimum spanning tree (MST) para encontrar el minimo numero de enlaces en una zona georreferenciada especifica. Al estar georreferenciada se logra que la localizacion de UDAP y la topologia de la red sean reales. La busqueda de los lugares optimos de un conjunto de sitios candidatos advierte la necesidad de un algoritmo de optimizacion que permita minimizar los sitios candidatos y los convierta en sitios factibles activos en el diseno previo al despliegue de la red. Los resultados obtenidos implican un punto base firme para que las empresas de distribucion electrica establezcan el numero adecuado de concentradores (UDAP). Dentro del trabajo se consideran ciertas tecnologias de comunicacion aplicadas en AMI como: redes celulares (CDMA, GPRS, LTE) o el estandar IEEE 802.15.4 g. El menor costo basado en el uso optimo de recursos para despliegue de infraestructura toma gran importancia para la planificacion y escalabilidad de la red de AMI. EnglishThis paper optimizes the cost of deployment of universal data aggregation points (UDAP) for advanced smart metering infrastructure (AMI), considering coverage and capacity. We propose a routing model between UDAPs, based on minimum spanning tree algorithm (MST) to find the minimum number of links in a specific geo-referenced area. Because it is geo-referenced, the location of the UDAP and the topology of the net are real. The search for optimal locations of a set of candidate sites warns the necessity of an optimization algorithm to minimize candidate sites and turn them into active feasible sites in the previous design to the network deployment. Results imply a strong base point for electricity distribution companies to establish the right number of concentrators (UDAP). There are certain applied AMI communication technologies considered, such as: cellular networks (CDMA, GPRS, and LTE) or the IEEE 802.15.4 g standard. The lowest cost based on the optimal resources usage in infrastructure deployment is very important for planning and scalability phases of AMI network

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Óptimo despliegue y enrutamiento de UDAP para infraestructura de medición avanzada basada en el algoritmo MST

Stratified sampling is one of the most commonly used sampling strategies for soil survey and mapping. An important issue in stratified sampling is the sample size allocation for each stratum. Neyman allocation is a typical sample size allocation approach in which the main idea is to assign more samples to a stratum with a larger internal standard deviation. By applying the spatial variability for sample size allocation, we proposed a new allocation approach (SVNA) for stratified spatial sampling. In this new approach, more samples were allocated to strata with both a bigger statistical standard deviation and larger spatial variability of the target variable. SVNA was evaluated using a simulated experiment based on real-world soil data of Jiangsu province, China. The target variable was soil organic matter (SOM), and land-use type was utilized for stratification. Simple random sampling (SRS) and spatial simulated annealing algorithm (SSA) were applied to determine sample locations. Results showed that the performance of SVNA generally was superior to Neyman allocation in terms of SOM content prediction at all sampling densities (4.28–0.19 points per km2) regardless of the application of SRS or SSA for sample location determination. The maximum reduction of mean squared error (MSE) for the SOM content prediction using SVNA was 11% using SRS and was 10% using SSA. Compared with Neyman allocation, SVNA obtained greater prediction accuracies in strata with large spatial variability and similar or slightly smaller prediction accuracies for strata with small spatial variability. Furthermore, the prediction accuracy of the SVNA approach tended to decrease when the sampling density was 0.37 points per km2, and this sampling density threshold was not the same for each stratum. We concluded that SVNA is an efficient sample size allocation approach for stratified sampling in soil survey and mapping.

Spatial variability-based sample size allocation for stratified sampling

Developing a low-cost weather monitoring system for data-sparse regions of the Himalayas

. minimum spanning tree (MST) of a connected, undirected and weighted network is a tree of that network consisting of all its nodes and the sum of weights of all its edges is minimum among all such possible spanning trees of the same network. In this study, we have developed a new GIS tool using most commonly known rudimentary algorithm called Prim’s algorithm to construct the minimum spanning tree of a connected, undirected and weighted road network. This algorithm is based on the weight (adjacency) matrix of a weighted network and helps to solve complex network MST problem easily, efficiently and effectively. The selection of the appropriate algorithm is very essential otherwise it will be very hard to get an optimal result. In case of Road Transportation Network, it is very essential to find the optimal results by considering all the necessary points based on cost factor (time or distance). This paper is based on solving the Minimum Spanning Tree (MST) problem of a road network by finding it’s minimum span by considering all the important network junction point. GIS technology is usually used to solve the network related problems like the optimal path problem, travelling salesman problem, vehicle routing problems, location-allocation problems etc. Therefore, in this study we have developed a customized GIS tool using Python script in ArcGIS software for the solution of MST problem for a Road Transportation Network of Dehradun city by considering distance and time as the impedance (cost) factors. It has a number of advantages like the users do not need a greater knowledge of the subject as the tool is user-friendly and that allows to access information varied and adapted the needs of the users. This GIS tool for MST can be applied for a nationwide plan called Prime Minister Gram Sadak Yojana in India to provide optimal all weather road connectivity to unconnected villages (points). This tool is also useful for constructing highways or railways spanning several cities optimally or connecting all cities with minimum total road length.

/pdf/development-of-gis-tool-for-the-solution-of-minimum-spanning-3vacngdvt5.pdf

Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm

Assessing and transferring soil health information in a hilly terrain

Spatial interpolation is the process of estimating value of continuous target variable at unknown location based on available samples. At present, there are many interpolation techniques available, and each technique has its own pros and cons. Accuracy of interpolation mainly depends on (1) sampling pattern and number of samples, (2) interpolation model adopted, and (3) presence of co-variable if a number of sample points are less. There are certain sampling techniques available, namely, regular, random, stratified, cluster, etc. Due to complex topography of mountain ecosystem like the Himalaya, stratified sampling technique is supposed to give the best prediction. But due to high variability in elevation and remote locations in mountain regions, installation of automatic weather stations (AWS) as per stratified sampling method is very difficult. So mountain regions face lack of sufficient number of samples/observations for accurate prediction (Stahl et al. 2006). There are many interpolation techniques available, but they are mainly classified into two categories: deterministic and geostatistical techniques. Deterministic techniques are based on the geometric properties of the samples, whereas geostatistical techniques are based on geometric as well as spatial autocorrelation of the target variable. Some of the deterministic techniques are inverse distance weighted (IDW), spline, Thiessen polygon, and linear regression, and geostatistical techniques are simple kriging, ordinary kriging, universal kriging, co-kriging, regression kriging, indicator kriging, etc. Stationarity, isotropy, intrinsic hypothesis, and unbiasedness are the basic assumptions of geostatistical techniques (Sluiter 2009).

Comparison of Geostatistical and Deterministic Interpolation to Derive Climatic Surfaces for Mountain Ecosystem

Location-Allocation Problem (LAP) is a combinatorial optimising network problem which has been widely studied by operational researchers due to its many practical applications. In real life, it is usually very hard to present the customer’s demands in a precise way and thus they are estimated from historical data. There are so many types of the methods like exact, heuristic and metaheuristic methods to solve this problem to get the optimal and near optimal solutions. In this study, a metaheuristic approach is applied in GIS environment which gives quick and near optimal solution. To achieve this, one case study based on supply and demand of milk from Vita Distributors to Vita Booths in Hisar City, Haryana have been performed. In this study, the effectiveness and robustness of the metaheuristic algorithm was tested over a GIS geodatabase based network dataset consisting of road network (line features) and facility and customers locations (point features). The performance of the algorithm was also checked for two types of impedance factors i.e. time and distance. The results of this location-allocation problem are very much satisfactory in term of minimization of total transportation cost in providing high quality service locations (Vita Booths) for milk distribution.

Geographic Information System Based Solution for Location Allocation Problem for Finding High Quality Service Locations

Structured grids have drawbacks such as cell distortion and artefacts leading to numerical accuracy reduction during simulations. Spatial support size had proven its importance in various estimation, and this is where unstructured grid comes into action. Performing geostatistical simulation on unstructured grid requires accounting of support size effect. In this article, two methods have been used for accounting the support size effect, one is the classical fine-scale simulation approach and the other approach is using Discrete Gaussian Model (DGM). Each method is applied to generate simulated Digital Elevation Model (DEM) (as used in this study) and the resultant DEMs are studied to understand the effect of spatial support size on surface flow estimation of water. It was observed that due to regularisation in the data of unstructured grid the flow velocity and water surface elevation of the unstructured DEM gave a similar behaviour than the structured DEM. The minimum RMSE for water surface elevation is 0.83 m for unstructured DEM while the minimum RMSE for flow discharge is 0.38 m3/s. The maximum coefficient of determination of flow channel velocity and water surface elevation is 0.709 and 0.86 respectively. The results suggested that the unstructured DEM generated using DGM approach shows a high correlation to reference DEM used in this study than the simulated structured DEM. The surface flow output it was inferred the flow variation in both structured and unstructured DEM is affected not only by the vertical resolution of DEM but also by the horizontal resolution.

Hari Shankar

Papers

Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm

Assessing and transferring soil health information in a hilly terrain

Comparison of Geostatistical and Deterministic Interpolation to Derive Climatic Surfaces for Mountain Ecosystem

Geographic Information System Based Solution for Location Allocation Problem for Finding High Quality Service Locations

Comparison of surface water flow simulation over structured and unstructured grids