Application of computational and geocomputation techniques for geospatial analysis

The smart grid is widely considered as the advanced version of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the user's energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. Demand Response functions as a vital controller for the present electrical framework. From outsider see the interest reaction can be viewed as two connections, one related to customer viewpoint and the other is pertinent to energy supplier or market administrators. But both links are driven by the application of different computational techniques. Throughout the smart grid, the advanced computational technique has been properly utilized to enhance the outcome and capability of learning. The salient feature of this paper is to criticize the applicability of computational techniques for demand response. The paper explores major aspects such as DR issues, different computational techniques, and future extensions of demand response.

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

The impetus for the development of GIS technologies is derived primarily from the fact that traditional manual techniques for the derivation, organization, storage and use of spatial data are no longer sufficient to meet the demands and expectations placed on such activities. The GIS technology is a service technology used to assist spatial data management, evaluations and analyses. There are three basic approaches currently developed that organize spatial data in a format such that the computer can perform previously time-consuming analytical and display processes. These are: (1)the rectangular grid approach; (2)the fine or coarse polygon approach; and (3)the triangulated irregular network approach. There is a wide range of applications that can be supported by GIS. These can be reasonably divided into two groups: those that depend on qualitative analysis and those that depend on quantitative analysis. The GIS design requirements are much more demanding to support quantitative analyses.

This paper is based on a Multidimensional Fishery Time Series Database of Greece that stores spatial, biological, temporal, technical and economic data extracted from National Statistical Service of the country. Time series on fish species catch quantity are aggregated in order to form the spatial data on three levels: total, fish region and fish area. It provides consistent view of the spatial distribution of fish species catch quantities and allows a multi-scale analysis performance. Computational procedure and software techniques based on intersection of datasets are developed to extract specific features of catch by fish regions, areas and species, as well. Four case studies present analytical capabilities of computational techniques in order to find out significant differences and similarities on fish species catch by regions and by areas. This paper illustrates the use of public information sources on exploitation of natural resources for scientific analysis.

Event Abstract Back to Event Effective capacity-building for spatial analyses: Risk mapping for emerging infectious diseases in Indonesia Caitlin N. Pfeiffer1*, Mark A. Stevenson1, Farida Zenal2 and Luuk Schoonman2 1 Asia Pacific Centre for Animal Health, Faculty of Veterinary Science, University of Melbourne, Australia 2 Food and Agriculture Organization of the United Nations (Indonesia), Indonesia Emerging infectious diseases are an important threat to human and animal health worldwide, and many of these diseases are zoonoses. To understand the likelihood of disease emergence, spatial analyses are extremely valuable to facilitate targeting surveillance and risk mitigation activities to high-risk geographic areas. However, collecting and collating relevant existing data to facilitate spatial analyses can be challenging. Furthermore, relevant expertise in the spatial analytical methods may not be present in the countries at greatest risk for infectious disease emergence. Indonesia is known to be at increased risk for emerging vector-borne diseases and spill-over of diseases from wildlife into human populations1. The aim of this analytical and capacity-building project was to better understand the spatial variation in emerging infectious disease risk across the country, through upskilling of in-country staff to identify, gain access to, manage and analyse relevant existing data. Relevant spatial risk data included human demographic and health information, animal population distribution and health status, historical disease outbreak locations, and known disease drivers such as environmental data including temperature, rainfall, elevation, land use change over time and greenness indices. The project will produce risk maps that can be used to effectively communicate disease emergence risk with decision-makers, to facilitate policy decisions that can safeguard human, animal and environmental health. To facilitate the in-country analysis and interpretation of the data, a series of capacity building training activities were conducted in 2019. Five workshops were delivered in Indonesia to a team of trainees comprised of Government of Indonesia employees, academics from Indonesian universities and FAO Indonesia staff. The training courses focussed on the development of technical skills, providing practical exposure to analysis of spatial data, use of advanced software including R and WinBUGS, and the principles of risk mapping. Crucially, the delivery of this material as a series of workshops staggered over a three-month period allowed participants to build their skills and confidence in the relevant techniques over time. An important feature of this capacity-building activity was engagement of staff from multiple ministries of the Government of Indonesia (a One Health approach). This provided several important benefits, including insights about the nature and quality of existing data and knowledge of local factors that could potentially influence the spread of emerging infectious diseases. Additionally, the continued attendance of the same group of trainees facilitated the development of collaborative working relationships between staff from different Government of Indonesia ministries, providing a rich foundation for future collaborative and One Health work. Relevant local examples were used throughout the training activities which enhanced the trainees’ engagement with the technically challenging content. Evaluation at the end of each workshop indicated progressive skill development and increasing theoretical understanding throughout the series of courses. This series of workshops serves as a model for empowering more countries to deliver technically advanced analyses, to better protect human and animal health. The One Health approach was very beneficial for these spatial analyses. Expertise from a variety of disciplines contributed to a thorough understanding of each dataset and facilitated the selection of appropriate techniques. As sophisticated spatial analyses become increasingly feasible (through the collection and availability of data and increasing availability of software), it is important to consider how capacity to perform these analyses can be fostered in countries with previously limited technical expertise. Expert input is needed to determine which analytical techniques might provide answers to important research and policy questions. Expertise in training and adult education is also needed, to support technical staff in developing the knowledge and skills needed to competently perform the analyses. Substantial focus on the policy relevance, effective visualisation and communication of analyses, rather than just their technical features, will be required. The delivery of effective capacity-building activities, such as the example described in this work, will help ensure spatial analyses can be meaningfully implemented in the parts of our world where they are most needed. Acknowledgements This project is part of the Emerging Pandemic Threats Phase 2 (EPT-2) programme, funded by the U.S. Agency for International Development (USAID). We thank FAO Indonesia for their support of the program and all of the participating trainees for their dedication to the workshop material.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

The origins of modern spatial analysis lie in the development of quantitative geography and regional science in the late 1950s. The use of quantitative procedures and techniques to analyse patterns of points, lines, areas and surfaces depicted on analogue maps or defined by co-ordinates in two-or three-dimensional space characterise the initial stage. Later on, more emphasis was placed on the indigenous features of geographical space, on spatial choices and processes, and their implications for the spatio-temporal evolution of complex spatial systems. Spatial analysis, as it has become over the past four decades, is more than spatial statistics and data analysis, and goes far beyond data sampling, data manipulation, exploratory and confirmatory spatial data analysis, into areas of spatial modelling encompassing a large and diverse set of models in both the environmental and the social sciences. In the environmental sciences, they range from physical dispersion models (e.g. for suspended particulates), chemical reaction models (e.g. photochemical smog), and biological systems models (e.g. for ecosystems in water), through deterministic process and stochastic process models of regional environmental quality management, and integrated models for environment-energy-economic assessment. For many models, especially the physical dispersion models, chemical reaction models, and biological systems models, the geographical location of the site is not considered to be of overriding importance. The variation in model results over space can be achieved by obtaining different inputs to the area in question. Process models include deterministic versions which attempt to describe a particular sequence in terms of known physical laws, and stochastic model versions which aim to describe a particular process such as erosion, groundwater movement and absorption of pollutants in terms of probability theory. For such processes, there is an interaction between the spatial process and a substrate. This substrate provides a one-, two-or three-dimensional framework within which the process model can operate. The space in which the process is modelled is disaggregated into a set of finite elements which are usually assumed to be internally homogeneous. Two-dimensional models generally use as their data small pixels (see Fischer et al., 1996).

Recently, the necessity of new methods of spatial data integration and analysis in CRM has been increased since it is acknowledged that about eighty percent of all data stored in corporate databases has a spatial component. But conventional CRM systems are either incapable of managing spatial data or are not user-friendly when doing so. This paper has designed and implemented spatially-enabled integration management system that can manage consistently both enterprise and spatial data through a legacy CRM system and object-oriented database and additionally support spatial analysis and map visualization for a gCRM. Through implementation, it is demonstrated that the proposed system can facilitate effectively spatial data management and analysis in a legacy CRM system.

Geographic information systems (GISs) are widely used for representation, management, and analysis of spatial data in many disciplines. In particular, geoscientists increasingly use these tools for data integration and management purposes in many environmental applications, ranging from water resources management to the study of global warming. Beyond these capabilities, geoscientists need to model and simulate three-dimensional (3D) dynamic fields and readily integrate those results with other relevant spatial information in order to have a better understanding of the environmental problems. However, GISs are very limited for the modeling and simulation of spatial fields, which are mostly 3D and dynamic. These limitations are mainly related to the existing GIS spatial data structures that are static and limited to 2D space. In order to overcome these limitations, we develop and implement a new kinetic 3D spatial data structure based on Delaunay tetrahedralization and a 3D Voronoi diagram to support a 3D dynamic field simulation within GISs. In this article, we describe in detail the different steps from discretization of a 3D continuous field to its numerical integration, based on an event-driven method. For validation of the proposed spatial data structure itself and its potential for the simulation of a dynamic field, two case studies are presented in the article. According to our observations, during the simulation process, the data structure is maintained and the 3D spatial information is managed adequately. Furthermore, the results obtained from both experiments are very satisfactory and are comparable with the results obtained from other existing methods for the simulation of the same dynamic field. To conclude, we discuss the current challenges related to the development of the 3D kinetic data structure itself and its adaptation to 3D dynamic field simulation and suggest some solutions for its improvement.

Mound tombs, popular in the south Yangzi River area in Shang and Zhou Dynasties, are regional cultural remains in China. With the aims of protecting and scientifically analysing cultural relics, laser scanning technology was adopted to study Zhaihuatou mound tombs located in Nonglin Village in Tianwang town, Jiangsu Province. Multiple tombs are held within one mound in good keep and with the typical construction of centripetalism. Accurate tomb LiDAR (Light Detection And Ranging) data were acquired by applying terrestrial laser scanning technology to the field mound excavation. Subsequently, a spatial morphological analysis of the tombs was conducted on the basis of archaeological rules and GIS spatial data processing methods. Using the theory of centripetalism construction of multi-tomb-one-mounds, we proposed an algorithm to determine the concentrated area of the geometric directions of tombs, and centripetalism theory was scientifically validated in comparing results with the excavation data. Spatial data clustering methods were used to analyse and deduce the spatial distribution characteristics of the tombs. We propose and demonstrate that the burial system is in the form of family-clan aggregation, and is useful for developing research on regional burials. Experimental data show that the proposed method is a novel example of how spatial analysis can foster more precise field archaeological excavations on a large scale, and it is significant to study various types of tombs, relics and ruins.

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

Most ecological and genetic data show inherent structure in space, time, or phylogeny. This chapter focuses solely on spatial structure, acknowledging that the other types of dependency occur. The power of landscape genetics analyses to detect significant spatial relationships between genetic and landscape data is directly linked to the sampling design, the spatial analysis methods, and the genetic markers used. The chapter discusses the main issues of relating genetic variation to landscape predictors in the familiar context of regression analysis or, more generally, in the framework of the linear model. It then presents different approaches through which space can be incorporated into the linear model. Finally, the chapter explains two common goals of spatial analysis in landscape genetics: first, how to test for the presence of significant isolation-by-distance (IBD) and then how to account for IBD by incorporating it into the null model when testing for other landscape effects.

The relationship between spatial analysis and GIS has been a debating point for over a decade. Some see GIS as leading the way to a new era in which the desire and the ability to analyse spatial data is widespread, not just in geography but throughout all disciplines in which spatial data are encountered. Others see GIS as simply providing a medium for the recycling of out-dated spatial analytical techniques and models. This paper describes a role for GIS through the development of new forms of ‘local’ or ‘context-dependent’ spatial analytical methods in which the focus is on exceptions to the general trend represented by the more traditional ‘global’ methods.