Spatial Data Collection Research Articles

InSAR and machine learning reveal new understanding of coastal subsidence risk in the Yellow River Delta, China

Coastal subsidence is a geological disaster that has devastating consequences. However, an accurate understanding of its risks involves more than simply assessing the amount or rate of land subsidence. The existing methods used to evaluate geological disaster risks depend on extensive data collection, entail substantial workloads, suffer from error estimation challenges, and lack regional adaptability. These limitations prevent us from fully understanding coastal subsidence risks in estuarine deltas. Therefore, in this study, we propose a new subsidence risk assessment method that addresses the challenges of traditional geological risk assessments in terms of spatial coverage, spatiotemporal resolution, and data collection difficulty. First, Sentinel-1 multitemporal interferometric synthetic aperture radar (MT-InSAR) and cluster analysis were used to estimate the subsidence hazards. Subsequently, Landsat-8 imagery and a random forest (RF) classifier were used to obtain land use and land cover (LULC), and the analytic hierarchy process (AHP) was used to obtain settlement vulnerability. Thereafter, subsidence susceptibility was derived from the sediment layer thickness. By combining subsidence hazard, vulnerability, and susceptibility, the first subsidence risk map with a 30 m resolution was generated. The results showed that 4.54 % of the Yellow River Delta (YRD) area was high-risk, 8.75 % was medium-risk, and 10.14 % was low-risk. Notably, the risk map shows a clear overlap between high-risk and saltwater mining areas in the YRD. The proposed method is expected to improve our understanding of the coastal subsidence risk in estuarine deltas. Considering that the risk in high-value economic areas in the YRD is increasing, whereas the risk in low-value economic areas may change owing to human activity, early preventive measures are required.

Теоретико-методичні засади вебкартографування постмілітарних об'єктів

Purpose of the research objective is to develop and substantiate the theoretical and methodological foundations of web mapping of post-military facilities, taking into account the most effective approaches and techniques for collecting, analyzing, processing, and visualizing spatial data. The methodology includes using modern technologies to create accurate and informative interactive maps that provide not only the physical location of the objects but also detailed information, including textual, illustrative materials, graphical data, and 3D models. Results. Based on a comprehensive approach to researching, documenting, and presenting post-military facilities, the article elaborates the theoretical and methodological foundations of their web mapping. The relevance of using innovative digital technologies for creating interactive maps is discussed. The research methodology based on the collection, analysis and visualization of spatial data is considered in detail. The main principles and approaches to web mapping are highlighted, including historicity, systemic integrity, reliability, modernity, coherence, informativeness, presentability, accessibility, technological sophistication, variability of geographic bases, and partnership for effective project implementation. A technological scheme of web mapping is proposed, covering the stages of data collection, processing, and actual web mapping, ensuring a multidisciplinary and integrative approach to solving the set task. Designing and implementing web maps considering historical, cultural, technical data, and the current state of post-military facilities requires the interaction of specialists from various fields of knowledge. Scientific novelty and practical significance. The theoretical and methodological principles of interdisciplinary web mapping of post-military objects were developed for the first time. The principles and approaches to it have been defined, a technological scheme for web mapping of post-military objects has been developed. The proposed methodology and technology determine the practical significance of the research. Based on them, interactive maps of post-military facilities of any territory will be used in various fields of activity: education, science, preservation of historical and cultural heritage, tourism, promotion of the region.

Empowering Public Engagement in Spatial Planning: A Collaborative Mapping Approach

This paper explores collaborative mapping as an approach to enhance public engagement in spatial planning. It provides insights into the current state of the art in collaborative mapping for practitioners and policymakers aiming to utilize its potential for better spatial planning. Through a systematic analysis of relevant literature, it investigates the evolution, application, and challenges of collaborative mapping. A comprehensive search of relevant sources on Google Scholar reveals the growing interest in collaborative mapping, particularly since 2018, with a focus on topics like land use and accessibility. The review underscores the importance of democratizing spatial data collection and highlights the role of user-generated content in participatory approaches. Public participation in collaborative mapping is examined, emphasizing the inclusion of diverse stakeholders and the need for tailored approaches to accommodate varied motivations for engagement. Advantages such as cost reduction and data provision are balanced against challenges like data quality, semantic issues and accessibility barriers. Key dimensions of collaborative mapping, including cost reduction, data accuracy, semantic issues and stakeholder importance, are addressed in more detail.

A Study on the Development of Biotope Type and Evaluation Map of Gochang-gun

Gochang-gun, situated in Korea, has achieved the distinction of being the second city in the country to have all three UNESCO-designated natural environmentrelated World Heritage Sites, following in the footsteps of Jeju Island. UNESCO has conferred upon Gochang-gun the prestigious designations of a biosphere reserve, World Natural Heritage (Gochang-Buan mudflat), and World Geopark (Jeonbuk West Coast Geopark). Notably, the entire administrative district has been designated as a UNESCO Biosphere Reserve, signifying its role as a meticulously preserved region of outstanding natural beauty and ecological significance. Within this UNESCO Biosphere Reserve, the core areas encompass remarkable features, including the Gochang-Buan Mudflat, Ungok Wetland, Dolmen World Cultural Heritage sites, Seonunsan Provincial Park, and Dongrim Reservoir. In pursuit of a comprehensive ecological map of Gochang-gun, the National Institute of Ecology (NIE) conducted an extensive two-year ecological survey and biotope survey from 2021 to 2022. Ecological spatial data was meticulously compiled based on the results of these surveys. The resulting Biotope map provides detailed information on various attributes, encompassing Biotope types, Biotope grades, land cover status, land use status, and topographic details. This dataset is formally registered and rigorously managed, employing the Digital Object Identifier (DOI) system. The primary aim of this paper is to provide a comprehensive introduction to each attribute of the Gochang-gun Biotope map, which represents a detailed collection of spatial ecology data for the region. The intent is to make this data readily accessible for future research and studies, thereby advancing our understanding of Gochang-gun’s distinctive ecological and cultural heritage.

RESEARCH ON THE DIRECTIONS OF MONITORING THE USE OF REAL ESTATE IN SETTLEMENTS

Monitoring real estate assets in settlements is pivotal in effective territorial management. Accurate information on the status and utilisation of these assets is essential for informed decision-making, resource allocation, and the sustainable development of lands within settlements. This research delves into the multifaceted methodologies used to achieve these objectives. The driving force behind this research is the demand for data-driven, geolocation-based decision-making. The precise evaluation of real estate assets within settlements is indispensable for efficient land use planning, infrastructure development, and the prudent management of natural resources. In the ever-evolving urban landscape, comprehending the dynamics of real estate assets is strategically imperative, facilitating optimal resource allocation and sustainable growth. Populated areas often present intricate, multi-layered geographical landscapes. Conventional real estate monitoring methods, such as manual inspections and paper documentation, often fall short of the required timeliness and accuracy for effective management. It is here that modern technologies step in, offering innovative solutions to augment the monitoring process. Geographic Information Systems (GIS) are at the forefront of this transformation. They enable the collection, analysis, and visualisation of spatial data, offering a multidimensional perspective on real estate assets. Through GIS, real-time insights into asset usage, condition, and their impact on the environment become attainable. Utilising GIS empowers decision-makers and stakeholders, aiding informed choices that facilitate sustainable urban planning and development. Furthermore, integrating remote sensing technology elevates the monitoring of real estate assets in settlements. Satellite imagery and aerial photography provide high-resolution data to detect changes and trends. That is particularly valuable for assessing urban expansion, environmental shifts, and the allocation of lands for agriculture, industry, or residential purposes. Ensuring data accuracy, accessibility, and confidentiality is a paramount aspect of efficient real estate monitoring. Advanced data analytics, machine learning algorithms, and blockchain technology can be leveraged to safeguard data integrity, ensure privacy, and enhance the overall quality of information. In summary, research on monitoring the use of real estate assets in settlements stands as a critical field with wide-reaching implications. The amalgamation of GIS, remote sensing technology, and advanced data analytics is reshaping our approach to comprehending, managing, and planning land usage in urban and rural contexts. By harnessing the capabilities of modern technology and data-driven insights, we pave the way for more sustainable and efficient territorial management. Keywords: spatial provision, monitoring, geoinformation system, land use, cadastral data, modelling.

A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm.

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observation of spontaneous arm choice for different tasks, which can easily be influenced by instructions, presumed expectations, and awareness that one is being tested. To better quantify arm nonuse, we developed the bimanual arm reaching test with a robot (BARTR) for quantitatively assessing arm nonuse in chronic stroke survivors. The BARTR is an instrument that uses a robot arm as a means of remote and unbiased data collection of nuanced spatial data for clinical evaluations of arm nonuse. This approach shows promise for determining the efficacy of interventions designed to reduce paretic arm nonuse and enhance functional recovery after stroke. We show that the BARTR satisfies the criteria of an appropriate metric for neurorehabilitative contexts: It is valid, reliable, and simple to use.

Autonomous GIS: the next-generation AI-powered GIS

ABSTRACTLarge Language Models (LLMs), such as ChatGPT, demonstrate a strong understanding of human natural language and have been explored and applied in various fields, including reasoning, creative writing, code generation, translation, and information retrieval. By adopting LLM as the reasoning core, we introduce Autonomous GIS as an AI-powered geographic information system (GIS) that leverages the LLM's general abilities in natural language understanding, reasoning, and coding for addressing spatial problems with automatic spatial data collection, analysis, and visualization. We envision that autonomous GIS will need to achieve five autonomous goals: self-generating, self-organizing, self-verifying, self-executing, and self-growing. We developed a prototype system called LLM-Geo using the GPT-4 API, demonstrating what an autonomous GIS looks like and how it delivers expected results without human intervention using three case studies. For all case studies, LLM-Geo returned accurate results, including aggregated numbers, graphs, and maps.. Although still in its infancy and lacking several important modules such as logging and code testing, LLM-Geo demonstrates a potential path toward the next-generation AI-powered GIS. We advocate for the GIScience community to devote more efforts to the research and development of autonomous GIS, making spatial analysis easier, faster, and more accessible to a broader audience.

Perspectives on Advanced Technologies in Spatial Data Collection and Analysis

The motivation to organize this Special Issue originated from the observation of rapid changes taking place in the domain of geographical information science and systems over the past few decades [...]

Landscape planning as a tool for preserving the landscape values of the Dinara Nature Park

Abstract Dinara Mountain massif is recognized as an area of exceptional value, which led to its recent protection as a nature park, the second largest one in Croatia. The protected area includes the territory of two counties with four cities and nine municipalities, five of which are included with over 50% of the territory. The paper explains how landscape planning methods establish potential spatial conflicts and propose a compromise between landscape conservation and spatial development. The basic method was the identification and analysis of the landscape value models in 3 categories: natural, visual-experiential, and cultural-historical values. The creation of the models is preceded by a detailed analysis of all environmental components and the collection and creation of spatial data with the help of GIS tools. By overlapping the basic models, a cumulative model of the overall landscape values of the space is produced, which clearly shows which parts of the landscape have a higher value, regarding the protection requirements. The final value model is overlapped with the planned interventions and a clear overview of the vulnerability and possible conflict zones in the area is obtained. The paper describes the impact of individual interventions on the landscape qualities that need to be preserved and suggests guidelines for spatial plan corrections for the purpose of minimal impact on existing values, thus helping to prevent unwanted changes.

Geographical information systems in epizootological monitoring of parasitic infestations of carnivores with zoonotic potential

Recently, geographic information systems (GIS) have reached a high level of commercialization, wide distribution around the world and are being introduced into various fields of science, including medicine and veterinary medicine in order to visualize geospatial information to improve the quality of epizootological and epidemiological monitoring. A geographic information system is a system that provides the collection, storage, processing, access, display and distribution of spatial data. Us from March 2020 to January 2023 work was carried out to study the frequency of occurrence of toxocariasis in dogs and cats in the city of St. Petersburg and the Leningrad region. Based on the results of the study of the helminth fauna of carnivores, as well as veterinary reporting data provided by the City Veterinary Department using the Quantum GIS (QGIS) program, data on the distribution of zoonotic invasion of carnivores were visualized and it was found that toxocariasis in dogs is more often recorded in Moscow (14.3 %), Frunzensky (10.2%), Nevsky (13.05%) Krasnoselsky (6.7%) districts of St. Petersburg and Vsevolozhsky (12.05%) district of the Leningrad region, and in cats - in the Kalininsky district (15 %), Nevsky (12.5%), Moskovsky (8.33%), Krasnoselsky (7.32%) and Admiralteysky (7.03%) districts of St. Petersburg. Thus, with the help of GIS, it is possible not only to systematize the spatial data obtained during research, which simplifies management and work with them, but also to visualize the data obtained in the form of digital maps, which in turn makes it possible to quickly and clearly assess the epizootic situation for a particular disease and as a consequence, improve measures of control and (or) prevention of invasion.

Геоинформационное обеспечение строительства транспортных объектов

The article discusses the importance of geoinformation technologies for modern construction, and especially for transport facilities. The main focus is on the need to integrate geospatial data into the processes of designing, constructing, and operating transport facilities. Key components and tasks of geoinformation support are described, including planning, development of feasibility studies, collection of spatial data for research and monitoring of construction.

Spatial data collection and qualification methods for urban parks in Brazilian capitals: An innovative roadmap.

Urban parks have been studied for their effects on health and the environment. Accessing park data from reliable and comparable sources remains challenging, reinforcing the importance of standardized search tools, notably in Latin America. We designed a systematized methodology to identify processes of accessing, collecting, verifying, and harmonizing urban park spatial data in all Brazilian capitals included in the Urban Health in Latin America (SALURBAL) project. We developed a research protocol using official and non-official sources combining the results of Google Maps (GMaps) points and OpenStreetMap (OSM) polygons-GMaps-OSM. Descriptive analyses included the frequency of the distribution of parks before and after harmonization stratified by data source. We used the intraclass correlation coefficient (ICC) to assess agreement in the area between official and GMaps-OSM data. Official data were obtained for 16 cities; for the remaining 11 capitals, we used GMaps-OSM. After verification and harmonization, 302 urban parks were obtained from official data and 128 from GMaps-OSM. In a sub-study of the 16 cities with official data (n = 302 parks), we simulated a collection of non-official data using GMaps-OSM and OSM only. From GMaps-OSM, we obtained 142 parks, and from OSM, 230 parks. Statistical analysis showed a better agreement between official data and OSM. After completing verification and harmonization, the complete dataset (official and GMaps-OSM) included 430 urban parks with a total area of 145.14 km2. The mean number of parks across cities was 16, with a mean size area of 0.33 km2. The median number of parks was nine, with a median area of 0.07 km2. This study highlights the importance of creating mechanisms to access, collect, harmonize, and verify urban park data, which is essential for examining the impact of parks on health. It also stresses the importance of providing reliable urban park spatial data for city officials.

Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model's overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.

AtlasXplore: a web platform for visualizing and sharing spatial epigenome data.

In recent years, a growing number of spatial epigenome datasets have been generated, presenting rich opportunities for studying the regulation mechanisms in solid tissue sections. However, visual exploration of these datasets requires extensive computational processing of raw data, presenting a challenge for researchers without advanced computational skills to fully explore and analyze such datasets. Here, we introduce AtlasXplore, a web-based platform that enables scientists to interactively navigate a growing collection of spatial epigenome data using an expanding set of tools. https://web.atlasxomics.com.

Universal Geographic Information System Productivity beneath optimum Underwater Sensor Networks Routing

Study Framework: The system of geographical information (GIS) is a computerized method for collecting, storing, disseminating, querying, updating, processing and analyzing geographic data. (GIS) enables the collection of diverse spatial data from various sources and transforms it into an information board for multiple users to boost productivity. Intelligent techniques, remote sensing, and underwater Wireless sensor networks are applied to improve GIS dramatically. Remote sensing data sources for GIS come from underwater sensor networks, The implementation of effective sensing devices, intelligent mobile nodes, and a multi-hop routing system can facilitate the efficient and optimal routing of traffic towards the basin or base station. They have been using GIS, which has revolutionized response scenarios with its ability to collect, analyze and manipulate spatial points. The objective of the study: The aim of this research is to differentiate the significant routing protocol issues that are relevant to underwater sensor networks. These networks serve as key transceivers that have established mechanisms for seamless and efficient data transmission. The mechanisms: The article presented a performance evaluation simulation based on a comparison of five different routing protocols that perform to promote GIS execution. A rigorous comparison of the outcomes varied by five routing protocols, where it evaluated with random mobility UnderWater sensor model based on the varied network parameters, was performed to validate this work. The continued to improve results: The suggested study in this manuscript contains spatial-information services universal to routing protocols to aid a group of navigators in harsh circumstances. According to the findings of the study, the CTP-ABS-SEEC routing protocol demonstrated superior performance compared to the other four routing protocols (CTP-SEEC, SEEC, EEDBR, and DBR) in the specified network configuration.

Assessment of Using UAV Photogrammetry Based DEM and Ground-Measurement Based DEM in Computer-Assisted Forest Road Design

Computer-assisted forest road design mainly relies on a high-resolution digital elevation model (DEM), which provides terrain data for supporting the analysis of road design features. The resolution and accuracy of the DEM in representing the terrain structures vary depending on the preferred dataset, which then reflects some of the essential road features such as alignment, road slope, and earthwork. In this study, three forest road sections were designed by using high-resolution DEMs generated from UAV photogrammetry data, GNSS-GPS data and Total Station data. NetCAD 7.6 software, developed in Turkey and mostly used in road design applications, was used to perform the road design while calculating horizontal profiles, vertical profiles, curves, cross sections, and earthwork. The DEM generation capabilities for three datasets were compared based on spatial resolution, data collection and data processing stage. Then, the differences between three road sections were evaluated by considering specified road features such as alignment properties, road slope, and earthwork. The results indicated that the UAV (Unmanned Aerial Vehicles) based DEM generation method provided the highest resolution (10 cm), followed by the Total Station (56 cm) and GNSS-GPS (61 cm) based methods. When comparing the time for data collection procedure, it took 14 minutes, 70 minutes, and 110 minutes for UAV data, GNSS-GPS data, and Total Station data, respectively. On the other hand, UAV based method falls into a disadvantageous situation in data processing stage, due to high data processing time (3 hours). However, GNSS-GPS and Total Station based methods work only with spatial point data, so they require less processing time of 15 minutes and 25 minutes, respectively. The results indicated that road lengths were 294.8, 272.4 and 282.1 m and the average road slopes were 3.41%, 3.39%, and 3.31% for the road sections designed by using UAV, GNSS-GPS, and Total Station based DEMs, respectively. The excavation and landfill volumes were 369.16 m3 and 166.98 m3, 285.86 m3 and 201.83 m3, and 433.17 m3 and 183.95 m3, respectively. The results indicated that UAV photogrammetry data generates high-resolution DEMs that can be effectively used to design forest roads.

Application of UAV for Village Mapping in Pangambatan, Sumatra Utara

Pangambatan Village, as part of the Lake Toba National Strategic Area and a tourist destination, still lacks facilities, infrastructure, and professional human resources to manage its cultural and tourism potential. An initial mapping of the conditions and potential of the area is needed to drive tourism in Pangambatan Village towards sustainable tourism. This can be done by mapping environmental conditions and tourism potential in Pangambatan Village, providing recommendations for sustainable tourism management, and providing environmental management facilities. The study was carried out using Unmanned Aerial Vehicle (UAV) technology. UAV technology offers new opportunities for fast and efficient remote environmental monitoring and the collection of high spatial and temporal resolution data. The benefits of this mapping can be used to see environmental conditions and environmental pollution that may occur because of tourist activities. In addition, the use of this technology is expected to help related parties in making decisions and planning.

MOSAIC: Multi-Omic Spatial Atlas in Cancer, effect on precision oncology.

e15076 Background: Precision oncology aims to first break diagnoses into biologically distinct subtypes, then pursue personalized therapies for each group of patients ( Garraway et al, J Clin Onc, 2013). This strategy is enabled by recent advances in technologies for medical imaging, molecular profiling, and artificial intelligence. Spatially resolved molecular profiling (“spatial omics”) is an emerging technology that harnesses all three of these advances ( Navarro et al, Science, 2016; Rao et al, Nature, 2021). Cancer is driven by localized interactions between tumor cells, immune and non-immune stromal components. Methods: Spatial omics studies have revealed specific cell types and pathways that control those tumor-microenvironment crosstalks ( Hunter et al, Nat Comm, 2021; Alon et al, Science, 2021), involving both innate and adaptive immunity ( Binnewies et al, Nat Med, 2018). Identifying new cancer subtypes based on the spatially localized tumor-host interactions across many patients have the potential to revolutionize immuno-oncology. The advances in utilizing AI-based methods have so far been unattainable due to a small number of patients in spatial omics studies. Results: Here we present the MOSAIC project - a collaborative initiative across industry and top oncology hospitals to build the largest collection of spatial omics data in cancer. We combine comprehensive clinical annotation with new deep profiling methods to both discover cancer subtypes and identify drug targets and biomarkers within them. MOSAIC aims to generate multimodal data for a total of 7,000 patients in seven cancer indications. Data modalities will include spatial and single cell transcriptomics and proteomics, bulk molecular profiling, pathology images, and curated clinical information. This collaboration harnesses the strengths of academia and industry to provide patient samples, generate high-quality data, develop AI-based analytical tools, and compile a resource that will eventually be made widely available for medical research. Conclusions: Here we present the workflow of the MOSAIC study, demonstrate the data processing pipelines and show spatial distributions of transcripts within the tumor microenvironment. Building upon these initial datasets, we discuss how the MOSAIC parties will collaborate to build an unprecedented database for research and discovery of new immuno-oncology therapeutic approaches.

Review of Photogrammetric and Lidar Applications of UAV

Using Unmanned Aerial Vehicles (UAVs) combined with various sensors brings the benefits associated with fast, automatic, and contactless spatial data collection with high resolution and accuracy. The most frequent application is the possibility of effectively creating spatial models based on photogrammetric and lidar data. This review analyzes the current possibilities of UAVs. It provides an overview of the current state of the art and research on selected parameters regarding their history and development, classification, regulation, and application in surveying with creating spatial models. Classification and regulation are based on national sources. The importance and usability of this review are also carried out by analyzing the UAV application with selected photogrammetric and lidar sensors. The study explores and discusses results achieved by many authors in recent years, synthesizing essential facts. By analyzing the network of co-occurring High-Frequency Words, in addition, we visualized the importance of the primary keyword UAV in the context of other keywords in the literary sources processed.

Applications of Robust Methods in Spatial Analysis

Spatial data analysis provides valuable information to the government as well as companies. The rapid improvement of modern technology with a geographic information system (GIS) can lead to the collection and storage of more spatial data. We developed algorithms to choose optimal locations from those permanently in a space for an efficient spatial data analysis. Distances between neighboring permanent locations are not necessary to be equispaced distances. Robust and sequential methods were used to develop algorithms for design construction. The constructed designs are robust against misspecified regression responses and variance/covariance structures of responses. The proposed method can be extended for future works of image analysis which includes 3 dimensional image analysis.