GCRM을 위한 공간 데이터 통합관리 시스템의 설계 및 구현

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.

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.

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.

Creation of a consistent 3D model of a city requires accurate data. Usually, accuracy assurance problems of data are solved by time consuming and expensive process of collecting and aligning with ground control points (GCP). Therefore, alternative methods become important. Using existing Geographic Information Systems (GIS) databases may decrease the time and cost of creating a reference dataset by reducing the number of GCPs required for producing high quality 3D data or GIS databases can serve as reference data. For this purpose, new spatial data analysis methods are needed to assure that GIS databases are of high-quality. In this paper, we propose a novel methodology and its sample development for comparative spatial analysis of digitized point cloud and the corresponding GIS database in order to statistically assess opportunities to align Mobile Mapping Systems (MMS) data with existing GIS databases or to improve involved datasets. The method is evaluated using LiDAR data provided by Estonian company Reach-U Ltd. and GIS database layers from different Estonian open and closed databases.

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.

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.

In this paper, we first apply the methods of exploratory spatial data analysis and investigate the geographical concentration of interrelated growing industries, or “growth clusters,” by using data from the Nagoya metropolitan area in Japan over the period 1986–2006. Second, by applying econometric models, we examine whether and which type of knowledge externalities contribute to region-industry dynamics and to the formation of the detected growth cluster. As a methodological contribution, following Porter’s (Reg Stud 37:549–578, 2003) widely recognized cluster concept, we incorporate spatial dependence caused by the geographical proximity between regions and the technological proximity between industries into the empirical models. Combining the information obtained from the ESDA and econometric analysis enables us to assess the role of knowledge externalities for regional growth from a cluster perspective. The empirical results identify the presence of a growth cluster mainly driven by the automobile and associated industries. The core of the cluster is made up of manufacturing industries, but also includes several service sectors. The periphery of the cluster has less variety, mainly including the transportation equipment and electrical machinery industries. We find that intra-industry externalities that diffuse over a broader area within the cluster help the steady growth of the transportation equipment industry. In the core of the cluster, the diversified interrelated structure also contributes to the growth of the auto-related manufacturing sectors.

Previous literature on childhood poverty in Puerto Rico is sparse. This is surprising since Puerto Rico exhibits very high poverty rates as compared to the rest of the United States. In this paper, we apply a structural perspective and consider how economic characteristics, household structure and migration patterns influence child poverty rates in Puerto Rico. Data for this paper come from the 2006–2010 Puerto Rico Community Survey (PRCS) summary files. We apply methods of exploratory spatial data analysis and spatial regression models to understand how municipio (county equivalents) level child poverty rates are influenced by these factors. A spatial modeling methodology was deemed appropriate since significant spatial structure is found for the child poverty rate and residuals from the Ordinary Least Squares model. Household composition, as measured by the percent of female headed households and the percent of grandparents caring for their own grandchildren consistently showed positive associations with child poverty. In terms of the economic sector variables, the proportion of the workforce in agriculture and proportion without a high school education showed significant positive effects on child poverty. With respect to migration, we find little to no impact, but we do find that child poverty is concentrated outside of the region adjacent to the capital of San Juan.

China has recently experienced an increase in the incidence of syphilis. Effective spatial monitoring of syphilis incidence is important for successful implementation of control and prevention programmes. This study monitored county-level primary and secondary (P&S) syphilis incidence rates for all of mainland China by examining spatial patterns. Exploratory spatial data analysis (ESDA) methods were used to characterize the spatial distribution pattern of syphilis cases. During the seven-year study period, the average annual P&S syphilis incidence was 8.82 cases per 100,000 people. Using Empirical Bayes smoothed rates, the local Moran test identified many areas of high syphilis risk (all P values ≤0.01). The number of high-risk counties increased from 73 counties in 2004 to 134 counties in 2010. An eastern coastal cluster of high-risk counties persisted throughout 2004-2010. ESDA methods can assist public health officials in identifying high-risk areas. Allocating more resources to high-risk areas could more effectively reduce syphilis incidence.

Exploring the causal relationships and pathways between ecological environmental quality and influencing Factors: A comprehensive 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.

Arsenic exposure through drinking water is a serious public health concern in the southern province of Pakistan. Little information on As exposure to children at school settings is available in Pakistan. The current study aimed to assess arsenic contamination through drinking water sources and estimate its health risk to the children, the potentially malnourished Pakistan population. We used risk assessment models to estimate the hazard quotient index and lifetime cancer risk. Spatial data analysis methods were used to investigate the spatial pattern of As contamination and its relationship with the area's hydrogeology. Across the 423 sampled schools, the drinking water exceeded the WHO permissible limits (19.6% for arsenic and 15% for iron). The arsenic's average incremental lifetime cancer risk (ILCR) exceeded the USEPA permissible limit. The arsenic hotspots were mainly located in the central districts. The present study's findings elaborate that the reduction is the controlling phenomenon in the lower Indus basin in the active flood plains, which is the primary source of fresh groundwater in Sindh and pH-induced dissolution is the second phenomenon observed only in the irrigated area, especially at the boundary of the hotspots. These findings are helpful to inform policymakers on measures to ensure prior treatment of As in the drinking water for the schools in areas adjacent to the riverbank.

A-level scenic spots are a unique evaluation form of tourist attractions in China, which have an important impact on regional tourism development. Guizhou is a key tourist province in China. In recent years, the number of A-level scenic spots in Guizhou Province has been increasing, and the regional tourist economy has improved rapidly. The spatial distribution evolution characteristics and influencing factors of A-level scenic spots in Guizhou Province from 2005 to 2019 were measured using spatial data analysis methods, trend analysis methods, and geographical detector methods. The results elaborated that the number of A-level scenic spots in all counties of Guizhou Province increased, while in the south it developed slowly. From 2005 to 2019, the spatial distribution in A-level scenic spots were characterized by spatial agglomeration. The spatial distribution equilibrium degree of scenic spots in nine cities in Guizhou Province was gradually developed to reach the “relatively average” level. By 2019, the kernel density distribution of A-level scenic spots had formed the “two-axis, multi-core” layout. One axis was located in the north central part of Guizhou Province, and the other axis ran across the central part. The multi-core areas were mainly located in Nanming District, Yunyan District, Honghuagang District, and Xixiu District. From 2005 to 2007, the standard deviation ellipses of the scenic spots distribution changed greatly in direction and size. After 2007, the long-axis direction of the ellipses gradually formed a southwest to northeast direction. We chose elevation, population density, river density, road network density, tourism income, and GDP as factors, to discuss the spatiotemporal evolution of the scenic spots’ distribution with coupling and attribution analysis. It was found that the river, population distribution, road network density, and the A-level scenic spots’ distribution had a relatively high coupling phenomenon. Highway network density and tourist income have a higher influence on A-level tourist resorts distribution. Finally, on account of the spatiotemporal pattern characteristics of A-level scenic spots in Guizhou Province and the detection results of influencing factors, we put forward suggestions to strengthen the development of scenic spots in southern Guizhou Province and upgrade the development model of “point-axis network surface” to the current “two-axis multi-core” pattern of tourism development. This study can explain the current situation of the spatial development of tourist attractions in Guizhou Province, formulate a regulation mechanism of tourism development, and provide a reference for decision-making to boost the high-quality development of the tourist industry.

With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents’ well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691.

BackgroundArea-level variation in treatment and outcomes may be a potential source of confounding bias in observational comparative effectiveness studies. This paper demonstrates how to use exploratory spatial data analysis (ESDA) and spatial statistical methods to investigate and control for these potential biases. The case presented compares the effectiveness of two antipsychotic treatment strategies: oral second-generation antipsychotics (SGAs) vs. long-acting paliperiodone palmitate (PP).MethodsA new-start cohort study was conducted analyzing patient-level administrative claims data (8/1/2008–4/30/2011) from Missouri Medicaid. ESDA techniques were used to examine spatial patterns of antipsychotic prescriptions and outcomes (hospitalization and emergency department (ED) visits). Likelihood of mental health-related outcomes were compared between patients starting PP (N = 295) and oral SGAs (N = 8,626) using multilevel logistic regression models adjusting for patient composition (demographic and clinical factors) and geographic region.ResultsESDA indicated significant spatial variation in antipsychotic prescription patterns and moderate variation in hospitalization and ED visits thereby indicating possible confounding by geography. In the multilevel models for this antipsychotic case example, patient composition represented a stronger source of confounding than geographic context.ConclusionBecause geographic variation in health care delivery is ubiquitous, it could be a comparative effectiveness research (CER) best practice to test for possible geographic confounding in observational data. Though the magnitude of the area-level geography effects were small in this case, they were still statistically significant and should therefore be examined as part of this observational CER study. More research is needed to better estimate the range of confounding due to geography across different types of observational comparative effectiveness studies and healthcare utilization outcomes.Electronic supplementary materialThe online version of this article (doi:10.1186/1472-6963-14-355) contains supplementary material, which is available to authorized users.