Spatial Data Analysis Tools Research Articles

Abstract LB252: iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data

Abstract Introduction The increased use of tissue-based multiplex immunohistochemistry (mIHC) in cancer research has significantly enhanced the generation of spatial hyper-plex images, crucial for understanding complex tumor-immune microenvironments. This advancement has led to a growing demand for spatial data analysis tools that enable quick and insightful evaluation. While existing spatial analysis software like Steinbock and SPIAT offer foundational support, effectively managing the high-dimensional and voluminous cellular data to derive meaningful biological insights remains challenging. This is especially true for researchers lacking bioinformatics expertise who seek to conduct quick preliminary evaluations. To address this gap, we introduce iCellSight: a user-friendly, code-free interactive analysis and visualization tool tailored to empower researchers with rapid and intuitive interpretation of in-situ cellular data. Material and methods A formalin-fixed, paraffin-embedded colorectal carcinoma (CRC) patient tissue section was subjected to staining using COMET (Lunaphore, Switzerland), which automates sequential immunofluorescence staining and imaging of up to 40 protein markers. The stacked image file produced was analyzed with HALO (Indica Labs, USA), which generated cell-level data like cell coordinates, stain intensity values and user-defined cell types. We developed a Shiny App tool, using the Shiny R package, with key features for cell data interpretation: (1) user-input cell number subsampling; (2) dimensional reduction plots with PhenoGraph for unsupervised clustering, allowing a choice of UMAP or t-SNE, and neighbor number selection of 30, 50 or 150; (3) 2D tissue space mapping of PhenoGraph clusters; and (4) heatmap visualizations displaying cluster-wise biomarker expression (with customizable color schemes and scaling), biomarker co-expression, and cluster pair-wise distance. Results Using iCellSight, 297,000 cells were detected from the CRC sample, where 27 clusters were identified. We observed 3 distinct clusters from the t-SNE plot that consist of macrophages, granulocytes, and NK cells and monocytes respectively. From the cluster projection in 2D tissue space, the cluster of NK cells and monocytes was found to be densely populated in a particular area of the tumor nest. Conclusion We presented iCellSight, a code-free visualization and analysis tool which streamlines the semi-automated analysis of spatial hyper-plex protein data. Designed with an intuitive user interface, this app enables researchers to efficiently navigate its features without requiring any prior coding expertise. We anticipate that the visualizations and analysis provided by this tool will significantly assist researchers in their data analyses and facilitate the validation of experimental hypotheses. Citation Format: Yancun Zhu, Felicia Wee, Menaka Priyadharsani Rajapakse, Esther Gek Teo, Nicholas Ang, Solomonraj Wilson, Zheng Yi Ho, Willa Wen-You Yim, Li Yen Chong, Craig Ryan Joseph, Jeffrey Chun Lim, Zhen Wei Neo, Chwee Ming Lim, Bernett Lee, Olaf Rotzschke, Joe Yeong, Mai Chan Lau. iCellSight: An interactive tool for analyzing and visualizing in-situ high-plex cellular protein data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB252.

Implementation of digital gis models in desktop applications developed on the basis of the «QT» framework

This article explores the implementation of digital geoinformation systems (GIS) models in desktop applications using the Qt framework. This highlights the importance of GIS technologies in various fields and the need for powerful desktop applications capable of efficiently processing spatial data. Integration of GIS models into Qt-based applications provides a number of advantages, such as efficient data management, spatial analysis and user-friendly interfaces. The article highlights the cross-platform compatibility of the Qt framework, which allows you to deploy GIS-enabled applications on multiple operating systems. In conclusion, it should be noted that the introduction of digital GIS models into desktop applications developed using the Qt framework provides significant benefits to both developers and users. This allows users to access, analyze and visualize geospatial data, thereby improving decision-making processes. The combination of GIS and Qt capabilities allows developers to create reliable and user-friendly applications to meet the growing demand for spatial data management and analysis tools. The list of sources used includes links to the official Qt and Esri websites, as well as relevant publications related to integration, GIS and noise mapping using ArcGIS.

Spatial patterns on tourism establishments in five CIP´s in Mexico, 2010–2022

AbstractStudies on the spatial patterns of economic activity contribute to economic performance management, planning, and promotion. Investors, policymakers, and social organizations have access to valuable information on the concentration and location of activity. Spatial patterns of tourism have been studied to better organize tourist flows, optimize existing infrastructure, plan new facilities, and promote new destinations. In the late 1960s, the Central Mexican government started projects aimed at constructing large tourism facilities primarily for foreign visitors. These were called centros integralmente planeados or “fully planned centers” (CIP’s) and were headed by Fondo Nacional de Fomento al Turismo (National Tourism Promotion Fund) FONATUR. The objective of this study is to explore the spatial distribution patterns of tourism establishments in five municipalities where CIP?s are located. The study uses exploratory spatial data analysis (ESDA) tools, such as average nearest neighbor index, standard deviation ellipse, spatial kernel density, global and local indicator of spatial autocorrelation, applied to geographic information system (GIS) data representing tourism establishments. The results show changes in establishment distribution and orientation, as well as spatial concentration in all cases and years. All CIP?s experienced a significant reduction in establishments, particularly small and medium-sized businesses. This paper is one of the first to analyze simultaneously the spatial distribution and concentration of tourism establishments in five municipalities where CIP’s are located.

Assessing the Level of Sustainable Development of Agricultural Regions in Central Kazakhstan based on Spatiotemporal Analysis of Land Use Dynamics

This study explores the methods and tools of spatial data analysis, including geographic information systems (GIS) and satellite data processing (SDP), for assessing changes in land use over time and space. The obtained results can be used to develop sustainable development strategies based on optimizing land resource utilization and accounting for ecological aspects in decision-making processes.

Geographic Information System Mapping of Public Works Infrastructure of Kebumen Regency

Geographic Information System (GIS) is an information system that combines graphical (visual) data with text data (attributes) of geographically referenced objects on the Earth's surface. GIS is widely used today in various fields, including analysis, industries, and individual activities such as finding locations or routes to specific destinations. In this research, GIS is used to manage the digitization of infrastructure mapping in Kebumen Regency. The development objective of the GIS is to process and analyze the spatial data of infrastructure locations to generate accurate information. The waterfall method is employed in the development of this system, encompassing stages of analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. The GIS infrastructure of Kebumen Regency facilitates the public in accessing information about the existing infrastructure in the area. The testing results using the black box testing method demonstrate that the system aligns with the defined requirements. The objective of this research is to develop a Geographic Information System (GIS) that can manage and analyze the spatial data of infrastructure in Kebumen Regency, resulting in accurate information. The waterfall method is used in the development of this system, with stages including analysis, design, implementation, testing, and maintenance using the CodeIgniter framework. The outcome of this research is the Geographic Information System for Mapping Public Works Infrastructure in Kebumen Regency based on Leaflet. With the GIS infrastructure of Kebumen Regency, the community can easily access information regarding the infrastructure in the area. This system enables users to visualize the locations of infrastructure and obtain attribute or text data related to those infrastructure objects. Through testing using the black box testing method, the system has been proven to meet the predefined requirements. This indicates that the developed GIS is reliable and provides accurate information about the infrastructure in Kebumen Regency. Thus, the development of this GIS provides significant benefits in managing spatial data of infrastructure and delivering accurate information to the public. This research has successfully achieved its objectives and produced a Geographic Information System that can be utilized as a tool for infrastructure mapping and spatial data analysis in Kebumen Regency.

Socio-Ecological Vulnerability in Aba Prefecture, Western Sichuan Plateau: Evaluation, Driving Forces and Scenario Simulation

With the social and economic development in recent years, human activities have been more extensive and intensified. As a result, ecosystems are damaged to varying degrees, and regional ecological environments tend to be weaker. The socio-ecological system in Aba Prefecture, Western Sichuan Plateau, China, the researched area, also faces increasingly serious problems. To advance ecological civilization development in a coordinated way across the country, the national government and the competent authorities have launched a series of new strategies. Research on socio-ecological vulnerability, a major part of the ecosystem protection and restoration program, is provided with powerful spatial data observation and analysis tools thanks to the invention and development of remote sensing and geographic information system technologies. This study was based on the vulnerability scoping diagram (VSD) framework. Multi-source data such as digital elevation model (DEM), geographical data such as land use types, soil and geological disasters, remote sensing image data, meteorological data and social statistics data from 2005 to 2019 were used to construct the temporal social-ecosystem vulnerability evaluation index database of Aba Prefecture, Western Sichuan Plateau. The spatial principal component analysis (SPCA) is applied to evaluating the socio-ecological vulnerability and analyzing its spatial-temporal variation in Aba Prefecture, Western Sichuan Plateau. To probe into the driving effects of various impact factors on the socio-ecological vulnerability, the Geodetector is used to analyze the driving factors. The ordered weighted average (OWA) method is applied to the multi-scenario analysis of socio-ecological vulnerability in the researched area. The conclusions of this study are as follows: (1) from 2005 to 2019, the spatial distribution characteristics of exposure and sensitivity in Aba Prefecture were higher in the southeast and lower in the northwest, and the overall spatial distribution characteristics of socio-ecological system vulnerability showed that the degree of vulnerability increased from the north to the southeast. (2) Extreme natural climate conditions play a leading role in the driving of socio-ecosystem vulnerability, followed by human production activities and geological hazards. (3) The degree of social-ecosystem vulnerability in Aba Prefecture will increase with the increase of decision risk coefficient. The results of social-ecosystem vulnerability under the status quo scenario are similar to those in 2010 and 2019, indicating that the selected evaluation factors can reflect the actual social-ecosystem vulnerability. In the sustainable guided scenario and the unsustainable guided scenario, the proportion of the area of the social-ecosystem severe vulnerability level was at the minimum value and the maximum value, respectively.

Efficient Retrieval of Top-k Weighted Triangles on Static and Dynamic Spatial Data

Due to the proliferation of location-based services, spatial data analysis becomes more and more important. We consider graphs consisting of spatial points, where each point has edges to its nearby points and the weight of each edge is the distance between the corresponding points, as they have been receiving attention as spatial data analysis tools. We focus on triangles in such graphs and address the problem of retrieving the top-k weighted spatial triangles. This problem is computationally challenging, because the number of triangles in a graph is generally huge and enumerating all of them is not feasible. To overcome this challenge, we propose an algorithm that returns the exact result efficiently. We moreover consider two dynamic data models: (i) fully dynamic data that allow arbitrary point insertions and deletions and (ii) streaming data in a sliding-window model. They often appear in location-based services. The results of our experiments on real datasets show the efficiency of our algorithms for static and dynamic data.

An Intelligent Analysis Model in the Environment of Regional Economic Development and Regional Economic Differences in China Using Big Data Analysis Technology.

The current and long-term regional economic imbalance in China requires ongoing attention. To ensure the balanced development of China's renewable energy, it is therefore important to examine the causes of the differences in China's renewable energy from a variety of perspectives. The spatial distribution pattern and characteristics of China's per capita GDP (gross domestic product) from 2012 to 2021 were examined in this study using the exploratory spatial data analysis tool. In addition, it conducts an empirical investigation into the spatial spillover effect of RED and the manufacturing agglomeration in China (regional economic development). The findings indicate that in the eastern region, the total backward link value of the feedback effect of 17 industrial sectors is 0.8524, and in the central region, the value is 0.8139. The real per capita GDP of neighboring provinces will increase by 0.118% for every 1% increase in manufacturing agglomeration level. According to the overall ranking, China's RED level is very uneven due to a number of factors. We should direct and encourage the manufacturing industry to congregate in various regions, optimize the spatial pattern of manufacturing industry agglomeration, and fully exploit SSE in order to promote China's RED and reduce the difference in RE.

Spatial variability of the sources and distribution of fluoride in groundwater of the Sanya alluvial plain aquifers in northern Tanzania

Groundwater contamination from geogenic sources poses challenges to many countries, especially in the developing world. In Tanzania, the elevated fluoride (F−) concentration and related chronic fluorosis associated with drinking F− rich water are common in the East African Rift Valley regions. In these regions, F− concentration is space dependence which poses much uncertainty when targeting safe source for drinking water. To account for the spatial effects, integrated exploratory spatial data analysis, regression analysis, and geographical information systems tools were used to associate the distribution of F− in groundwater with spatial variability in terrain slopes, volcanic deposits, recharge water/vadose materials contact time, groundwater resource development for irrigated agriculture in the Sanya alluvial plain (SAP) of northern Tanzania. The F− concentration increased with distance from steep slopes where the high scale of variation was recorded in the gentle sloping and flat grounds within the SAP. The areas covered with debris avalanche deposits in the gentle sloping and flat grounds correlated with the high spatial variability in F− concentration. Furthermore, the high spatial variability in F− correlated positively with depth to groundwater in the Sanya flood plain. In contrast, a negative correlation between F− and borehole depth was observed. The current irrigation practices in the Sanya alluvial plain contribute to the high spatial variability in F− concentration, particularly within the perched shallow aquifers in the volcanic river valleys. The findings of this study are important to the overall chain of safe water supply process in historically fluorotic regions. They provide new insights into the well-known F− contamination through the use of modern geospatial methods and technologies. In Tanzania's context, the findings can improve the current process of drilling permits issuance by the authority and guide the local borehole drillers to be precise in siting safe source for drinking water.

A demanda de automóveis no Brasil: uma análise a partir de efeitos regionais

O presente trabalho investigou a demanda de automóveis por mil habitantes nas microrregiões brasileiras por meio dos processos de convergência absoluta e condicional além de testar a hipótese da existência de clubes de convergência, no período 2004 a 2015. Investigou também, os efeitos regionais da redução do IPI (Impostos sobre Produto Industrializado) com o objetivo de identificar se o consumo de automóveis por mil habitantes a nível de regional sofreu alteração no período de atuação dessa política (2008 a 2014), em relação ao período anterior (2004 a 2007), os resultados foram obtidos por meio do uso de ferramentas de Análise Exploratória de Dados Espaciais (AEDE) e da Econometria Espacial. As estimações indicaram que microrregiões que apresentaram uma menor demanda efetiva por mil habitantes no período inicial (2004-2007) tenderam a ter maiores taxas de crescimento de suas frotas de automóveis no período posterior (2008-2014), evidenciando assim, um processo de homogeneização ao longo do território brasileiro. Ademais, os resultados indicam existência de dois clubes de convergência denominados de norte e sul, os quais apresentaram características distintas. Por fim, as variáveis estruturais mostraram-se satisfatórias tendo seu grau de significância em 1%. Esses resultados corroboram com aqueles encontrados pelos autores Cintra (2014), Haddad (2015), Lucinda (2017), que identificaram externalidades positivas do aumento da frota de automóveis e que têm o potencial de alavancar o dinamismo nas regiões vizinhas.

Geoinformation Technologies in Support of Environmental Hazards Monitoring under Climate Change: An Extensive Review

Human activities and climate change constitute the contemporary catalyst for natural processes and their impacts, i.e., geo-environmental hazards. Globally, natural catastrophic phenomena and hazards, such as drought, soil erosion, quantitative and qualitative degradation of groundwater, frost, flooding, sea level rise, etc., are intensified by anthropogenic factors. Thus, they present rapid increase in intensity, frequency of occurrence, spatial density, and significant spread of the areas of occurrence. The impact of these phenomena is devastating to human life and to global economies, private holdings, infrastructure, etc., while in a wider context it has a very negative effect on the social, environmental, and economic status of the affected region. Geospatial technologies including Geographic Information Systems, Remote Sensing—Earth Observation as well as related spatial data analysis tools, models, databases, contribute nowadays significantly in predicting, preventing, researching, addressing, rehabilitating, and managing these phenomena and their effects. This review attempts to mark the most devastating geo-hazards from the view of environmental monitoring, covering the state of the art in the use of geospatial technologies in that respect. It also defines the main challenge of this new era which is nothing more than the fictitious exploitation of the information produced by the environmental monitoring so that the necessary policies are taken in the direction of a sustainable future. The review highlights the potential and increasing added value of geographic information as a means to support environmental monitoring in the face of climate change. The growth in geographic information seems to be rapidly accelerated due to the technological and scientific developments that will continue with exponential progress in the years to come. Nonetheless, as it is also highlighted in this review continuous monitoring of the environment is subject to an interdisciplinary approach and contains an amount of actions that cover both the development of natural phenomena and their catastrophic effects mostly due to climate change.

Post-processing HAZUS earthquake damage and loss assessments for individual buildings

Building damage probabilities are invaluable for assessing short-term losses from natural hazards. In many countries however, the individual building level data required for assessing reliable damage are usually unavailable. This paper shows how the post-processing of aggregate HAZUS earthquake damage assessments can yield building-level damage probabilities. On the basis of three plausible scenarios for Northern Israel, we generate and visualize a building-level combined damage probability index. We use the tools of exploratory spatial data analysis to purge any causal influences in the spatial pattern of these calculated damage probabilities. The costs and benefits of our approach are discussed.

Proximal and contextual correlates of childhood stunting in India: A geo-spatial analysis.

BackgroundGlobally, India is home to every third child affected by stunting. While numerous studies have examined the correlates of childhood stunting (CS) in India, most of these studies have focused on examining the role of proximal factors, and the role of contextual factors is much less studied. This study presents a comprehensive picture of both proximal and contextual determinants of CS in India, expanding the current evidence base. The present study is guided by the WHO conceptual framework, which outlines the context, causes, and consequences of CS.Data and methodsThe study used exploratory spatial data analysis tools to analyse the spatial pattern and correlates of CS, using data from the fourth round (2015–16) of the National Family Health Survey (NFHS-4) and the 2011 Census of India.ResultsThe study findings reiterate that CS continues to be high in India, with several hot spot states and districts, and that children from the central and eastern region of the nation, namely, Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh are particularly vulnerable. Our analysis has identified six risk factors—maternal short stature, large household size, closely spaced births, prevalence of hypertension among women, household poverty, open defecation, and extreme temperature—and four protective factors—female education, access to improved drinking water, dietary diversity among children, and iron and folic acid (IFA) supplementation during pregnancy.ConclusionsThe study highlights the need for investing in pre-conception care, addressing both demand- and supply-side barriers to increase the coverage of nutrition-specific interventions, implementing programmes to promote the intake of healthy foods from an early age, providing contraceptive counselling and services to unmarried and married adolescents and young women and men, and universalizing quality primary and secondary education that is inclusive and equitable to avert the burden of childhood stunting in India.

Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

Uso del Índice de Moran y LISA para explicar el ausentismo electoral rural en Ecuador

En Ecuador, el voto es un derecho y obligación que constituye una parte esencial en la organización democrática al momento de escoger sus representantes que gobernaran el país, es así que dentro del marco geográfico electoral es importante analizar el ausentismo electoral especialmente en áreas rurales, cuyos escenarios son más propensos a este fenómeno debido a que existen varias dificultades en el acceso a recintos electorales cercanos, lo que acarrea largas distancias de transporte y altos costos para movilizarse. Por esta razón El Consejo Nacional Electoral del Ecuador ha creado jurisdicciones denominadas “zonas electorales rurales” con el fin de facilitar el acceso al voto.Para el presente trabajo se recurrió a dos herramientas de Análisis Exploratorio de Datos Espaciales denominadas: Índice de Moran e Índice Local de Asociación Espacial (LISA). El primero explica a nivel general como se encuentra distribuido el ausentismo en las zonas electorales rurales, la misma que puede expresarse en forma de agrupaciones, dispersiones o aleatoriamente. El segundo índice de LISA explica localmente el nivel de significancia estadística de los agrupamientos espaciales del ausentismo en las zonas electorales y como cada unidad espacial o zona electoral se diferencia o asemeja de las unidades espaciales vecinas.Por lo tanto este análisis recopila datos estadísticos y espaciales de las zonas electorales rurales creadas en los años 2009 y 2014 para entender la dinámica del porqué se crean zonas electorales y cómo éstas inciden en el ausentismo electoral.

Spatial Analyses of Temporary Assistance for Needy Families: Its Responsiveness to Labor Market Performance and Policies during the 2008 Recession

The major economic downturn of the Great Recession led many families with children to depend on cash assistance from the Temporary Assistance for Needy Families (TANF) program. In order to capture TANF caseload growth rates relative to their labor market performance, we developed a responsiveness index which revealed that many of the western and midwestern states were quite responsive to the recession. Using Exploratory Spatial Data Analysis (ESDA) tools, we found the presence of strong spatial clusters in unemployment rate and in TANF maximum aid. Several states in the western region were surrounded by neighboring states with higher growth in unemployment rate changes than the national average based on ‘Rook Contiguity’ weight matrix. The presence of High-High clusters of the level of TANF benefits was found in Northeastern states whereas the presence of Low-Low spatial clusters was found primarily in Southern states. The present study’s findings have confirmed the idea that spatial analyses can be useful for policy research. In the era of evidence-based policy making, the role of spatial analyses is bound to expand.

C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.

Spatial Dependency of Eco-Efficiency of Agriculture in São Paulo

This research presents an eco-efficiency index for the municipalities of Sao Paulo, indicating how much it would be possible to maximize economic and environmental objectives, taking into account the best practices for the municipalities of this region. In this vein, we used the Data Envelopment Analysis method with directional distance functions based on the classic variables of multiproduct production function and the internalization of two externalities (one positive and one negative). The study also used the tools of exploratory spatial data analysis to verify the spatial autocorrelation and spatial heterogeneity of the calculated index. The results indicate that, on average, the analyzed municipalities are able to expand the production and forested areas by 59% and also to reduce degraded areas and inputs in the same proportion. Spatial analysis demonstrated the existence of spatial heterogeneity and autocorrelation between municipalities and the formation of large clusters. Based on these results, priorities for environmental intervention in the state are defined.

Exploratory space data analysis of spatial patterns of large-scale retail commercial facilities: The case of Gulou District, Nanjing, China

This study uses methods, such as a nearest proximity index, nuclear density, spatial interpolation, buffering zone, and overlay analysis, based on an exploratory spatial data analysis tool. It focuses on a large commercial facility in which a mathematical analysis is conducted on its spatial patterns. In the study, 45 large-scale retail commercial facilities (LSRCFs) in the Gulou District, Nanjing, China, were chosen, and the spatial concentration, density, and structure of the LSRCFs in this area were analyzed. Three additional factors, namely, population, transportation, and consumption, were examined to determine their impact on the spatial patterns of the LSRCFs. Finally, this study recommends a spatial layout for the future of the Gulou District according to the analysis results.

GATEWAY: A GEOSPATIAL ANALYTICS SYSTEM

Abstract. This paper presents an overview of the Gateway web platform, a proprietary geospatial analytics system developed by Cobena Business Analytics and Strategy, Inc.1 The application is intended to serve as a user-friendly and easily-accessible tool for spatial data analysis and visualization geared toward non-technical specialists. Gateway’s core functionalities hinge on mapping and data visualization (choropleths and points) alongside traditional scoring methods and built-in machine learning algorithms for area prioritization and site selection. Gateway provides an interactive, cloud-based environment that abstracts and simplifies common location-based analyses. A core strength of the platform is also its heavy localization to the Philippine context through a curated database of market information — with future plans to create local counterparts across SEA — which reduces the need for extensive external market data collection and reconciliation. The paper gives a brief review of the system design and key features of the platform. It also highlights some key applications across industries such as real estate, consumer goods, and retail in informing expansion and distribution strategies, prioritizing resource allocation, and analyzing historical performance against market factors.