Context-dependent spatial analysis: A role for GIS?

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.

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.

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.

Computational science has significantly advanced theory and experiment over many decades. Specifically, computational geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which hindered deeper analyses of complex geographic data. The advent of relational databases further facilitated the use of computational methods in spatial data analysis. This study discusses the application of computational techniques and geocomputation in the spatial analysis of geographical phenomena. A literature search and data synthesis were conducted, followed by an exploration of computational methods, geocomputation, spatial data representation, storage and organization, spatial analytics, and GeoAI. Geocomputation is defined as the application of computer- intensive approaches, particularly those employing non-conventional data clustering and analysis methods, to discern knowledge. At present, these computational techniques enable the integration of diverse fields, supporting spatial analyses that require resources or ontological frameworks beyond the capabilities of traditional GIS software.

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.

Analyzing GIS technologies` products for strengthening the tourist attractiveness of the destination and research of possibilities of GIS-technologies at the formation of a tourist product is the purpose of this work. The relevance of this study lies in the need to apply innovative approaches to GIS technologies` usages in the development of national tourism products. The research methodology is based on the application of the statistical method of data processing, the cartographic method of research with the use of GIS, and the method of spatial analysis. General scientific, systematic and informational approaches are also used. The results of the study are an analysis of existing products of GIS technologies and the state of their implementation in tourism, the study of interactive maps and geoportals as tools for the successful solution of problems in the modern tourism sector. It has been found that geoportals significantly help to form a national infrastructure of geospatial data. In the publication I consider two levels of GIS implementation: national and regional. I gave vivid examples of newly created national geoportals and tourist geoportals of the regions of Ukraine, interactive maps of amateur tourists. I considered in more detail the development of GIS technologies` products of such a tourist destination as the Chernihiv region. In particular, as an example, an overview of one of the most functional in Ukraine geoportals - The Geoportal of the urban cadastre of the Chernihiv region is given. A thematic tour of the Chernihiv region was projected with the help of GIS. The scientific novelty of this study is identifying the features of the use of cartographic research methods, especially GIS technologies to study the tourist resources of the territory, the creation of new regional tourism products. I propose to use the method of spatial analysis in the creation of the thematic tour. The practical significance lies in the use of GIS technology products to create cartographic products, such as maps, series of maps, atlases, 3D models, virtual tours. Its help to study in detail the tourist objects of the region contribute to management decisions, and further development and promotion of tourism. The database created during the study can be used to form other types of tourism products. The proposed method of using spatial analysis in the creating of the tour can be useful for expanding the GIS technologies` field of usage.

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.

The division of internal structures and external space of geographical entities is foundation of spatial analysis, query and reasoning. Most current division methods are crisp, and inconsistent with human cognitive habits. Usually existing geographic information systems(GISs) analyze spatial data directly by certain spatial analysis methods and then use natural language or words to explain analysis results, so the encoding process is absence but it is necessary in intelligent GIS (IGIS). Fuzzy geographical entities and phenomena occur throughout the real world, and these semantics of words related spatial locations and relations usually involve uncertainties. First, the geographical perceptual computing (GPC) model based on CWW is proposed and it includes four modules: input, geo-encoder, geographical CWW engine and geo-decoder. Then, the trapezoidal fuzzy set is adopted to represent spatial words. A fine fuzzy spatial partitioning model of line objects based on CWW is proposed. The interior of a line object is divided into several parts according to fuzzy logic and human cognitive habits. The exterior of a line entity is then divided into several parts by combining direction relation and distance relation models with fuzzy logic methods. This model provides a full natural language description of the interior and exterior of crisp or fuzzy line entities and consistent with human cognitive habits. An application case of this model is provided in the last and proves the superiority of this model.

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.

Mechanic behavior of Y-shape thin-walled box girder bridge structure is complex, so one can not exactly hold the mechanical behavior of the Y-shape thin-walled box girder bridge structure through general calculation theory and analytical method. To hold the mechanical behavior better, based on elementary beam theory, by increasing the degree of freedom analytical method, taking account of restrained torsiondistortion angledistortion warp and shearing lag effect at the same time, authors obtain a thin-walled box beam analytical element of 10 degrees of freedom of every node, derive stiffness matrix of the element, and code a finite element procedure. In addition, authors combine the obtained procedure with spatial grillage analytical method, meanwhile, they build a new analytical method that is the spatial thin-walled box girder element grillage analysis method. In order to validate the precision of the obtained analysis method, authors analyze a type Y-shape thin-walled box girder bridge structure according to the elementary beam theory analytical method, the shell theory analytical method and the spatial thin-walled box girder element grillage analysis method respectively. At last, authors test a type Y-shape thin-walled box girder bridge structure. Comparisons of the results of theory analysis with the experimental text show that the spatial thin-walled box girder element grillage analysis method is simple and exact. The research results are helpful for the knowledge of the mechanics property of these Y-shape thin-walled box girder bridge structures.

: The principle objective of this project is to identify and implement methods for the analysis of error and uncertainty of spatial data in spatially explicit ecological models. The development of these methods for spatial error and uncertainty analysis is done in coordination with Dr. George Gertner and SERDP Conservation Thrust Area Project SC-1096. Methods and tools developped and implemented by our project will be transferred to Project CS-1096 for incorporation within that project's error budget framework and toolbox. Our software tools will also be compatible with the specifications and requirements of DoD and SERDP land management tools and software like the U.S. Army Corps of Engineers' Land Management System (LMS) or others. We have completed the first phase of a survey of existing methods for the analysis of error and uncertainty analysis in spatial data. From this survey we have formulated a general framework for spatial error and uncertainty analysis of ecological models, and we have implemented software tools for treatment of error and uncertainty in categorical spatial data. We have begun the process of testing and refining these software tools.

Spatial transcriptomics is a rapidly emerging field that allows for the study of gene expression within a tissue sample. This technology has the potential to provide valuable insights into the molecular mechanisms underlying cancer progression and could be particularly useful in identifying specific regional cancer progression. By studying the regional spatial trajectory of cancer progression, we can develop more targeted therapies and better predict the likelihood of metastasis. Additionally, analyzing gene expression patterns in specific regions of the tissue can reveal the molecular mechanisms driving cancer growth in those areas, potentially uncovering new therapeutic targets. In this study, we created a spatial trajectory analysis method, named as stLearn - PSeudo-Time-Space (PSTS) algorithm, to investigate specific regional cancer progression. Spatial trajectory analysis is a statistical method that allows for the identification of patterns of transitional gene expression over time and space within a tissue sample. In detail, this method incorporates spatial features, such as tissue sub-structure locations and gene expression changes, to model the trajectory from a single snapshot of transcriptomics data. Also, we evaluated the effectiveness of PSTS by utilizing the cancer progression model in human breast cancer samples. Using this approach, we were able to identify specific regions within the breast tissue that displayed distinct progression branching trajectories patterns of transitional gene expression associated with ductal carcinoma in situ and invasive ductal carcinoma. These regions were found to be characterized by a high level of cellular diversity and were associated with aggressive forms of breast cancer. The results of this study suggest that spatial transcriptomics and spatial trajectory analysis could be a powerful tool for identifying specific regional cancer progression and may help to inform the biomarkers for the development of targeted therapies for breast cancer. Overall, this study highlights the potential of spatial transcriptomics and spatial trajectory analysis to provide valuable insights into the molecular mechanisms underlying cancer progression and could have significant implications for the diagnosis and treatment of cancer. Citation Format: Duy Pham, Quan Nguyen. A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB076.

The paper examines the impact of R&D expenditures on gross domestic product (GDP) at the NUTS 1 level in Germany. Using the spatial panel data analysis method, the existence of spatial dependencies among regions was investigated, and the findings revealed positive spatial effects. Based on the tests conducted, the Random Effects Spatial Error Panel Data Model was identified and accepted as the appropriate model for this paper. It was determined that a one-unit increase in R&D expenditures leads to a 7% increase in GDP at a 1% significance level. Furthermore, the model was found to have an explanatory power of 63%, demonstrating that it is both statistically significant and reliable. The paper is significant as it emphasizes the multiplier effect that highlights the spatial impacts of R&D expenditures. Additionally, the heterogeneity among regions was found to have a meaningful spatial relationship with economic performance, leading to the development of an econometric model proposal in this context.

PurposeThe purpose of this paper is to describe how to measure workplace configuration, show its application in, and the results of, a field experiment aimed at improving collaborative knowledge work and identify and discuss larger problems involved with research on workplace configuration.Design/methodology/approachThe paper reviews recent thought on density, proximity and problems with evaluating configuration. It then describes a method of spatial network analysis used in a field experiment involving the reconfiguration of a workplace. This is followed with a discussion of recent research on knowledge work from economic geography.FindingsIt was found that, instead of increasing it, the reconfigured workplace decreased collaborative activity. The spatial network analysis shows how this occurred.Research limitations/implicationsThis method of spatial network analysis, when used carefully, is a robust technique for analyzing and comparing spatial configuration. Further research needs to address the links between spatial proximity and information and communications technologies as well as the relation of types of knowledge bases and associated forms of proximity that can stimulate collaboration.Practical implicationsWhile spatial network analysis methods are not do‐it‐yourself tools, corporate real estate managers should employ them, especially in larger‐scale projects, before committing to final workplace designs. They can also use them to identify and map best spatial patterns (like best practices) to identify strategic spatial patterns.Originality/valueThis appears to be the first rigorous application of spatial network methods in a field experiment involving a real workplace. The paper shows the method can clearly extract and discriminate spatial network patterns underlying configurations and relate them both quantitatively and graphically to employee evaluations of collaborative performance. It introduces concepts of comparative knowledge bases that need to be understood in determining the types of collaboration needed in a workplace.

Environmental degradation and increasing risks to regional eco-systems seriously impede the progress towards sustainable development goals. This study is aimed at investigating the spatial aspect of this problem by focusing on the case of Russia, in particular the environmental aspects of the country's spatial organization. Methodologically, the study relies on spatial analysis methods (spatial modelling and mapping) and the official statistical data. As a result, we have identified heavily polluted locations that jeopardize sustainable development efforts on the national level: as these locations are situated in close proximity to each other and share a strong spatial correlation, their combined impact far exceeds that of isolated territories. It was found that Siberia is the largest zone of concentration of air pollution hotspots, where high levels of air pollution are not compensated for by these regions’ outstanding economic performance. Our findings have shown that the patterns of the country's economic and environmental spatial organization do not always coincide. In other words, the regions with booming economies are not always the same regions whose manufacturing sector puts the heaviest pressure on the environment. This can point to the fact that the link between economic activities and environmental pressures is now weakening.