Spatial Science Research Articles

Evacuation intention on the Southern Chilean coast: A psychological and spatial study approach

Evacuation from tsunami hazards is a complex problem that requires a transdisciplinary approach, i.e., several topics must be interrelated (including the perspective of the population) to better understand the evacuation process. One of these involves the psychological response of people to the configuration and location of evacuation sites. This paper contributes to exploring the evacuation phenomenon by using social and spatial sciences, in order to test how psychological and spatial indicators influence intention to evacuate to designated sites in Puerto Saavedra, Chile. The methodology involved the assessment of photographs representing different tsunami evacuation scenarios for Puerto Saavedra: the current scenario, with evacuation sites in built areas and in natural environments without emergency facilities; and the hypothetical scenario, in the same sites but with emergency facilities in the natural sites. Photographs were evaluated using three psychological indicators and a Likert scale in a questionnaire. A multiple regression analysis was then performed using ten spatial indicators to explore the most significant spatial variables that explain perception in each scenario. Finally, a multi-criteria evaluation technique was used to model optimal evacuation site locations by integrating data from the questionnaire and the multiple regression analysis. Results show that perceived safety, place attachment and restorativeness vary between scenarios. Natural evacuation sites with facilities from the hypothetical scenario, located at higher elevations and far from the coastline, evacuation routes and emergency infrastructure received higher evaluation values for the psychological indicators. The discussion delves into the challenge of using these outcomes for resilient planning in Puerto Saavedra, and for improving evacuation research with knowledge about perception and real evacuation behavior of different community groups, as well as through studies considering the amount, location and qualities of evacuation routes and sites. This will deepen our understanding of evacuation processes and models in different contexts.

Geary’s c and Spectral Graph Theory

Spatial autocorrelation, of which Geary’s c has traditionally been a popular measure, is fundamental to spatial science. This paper provides a new perspective on Geary’s c. We discuss this using concepts from spectral graph theory/linear algebraic graph theory. More precisely, we provide three types of representations for it: (a) graph Laplacian representation, (b) graph Fourier transform representation, and (c) Pearson’s correlation coefficient representation. Subsequently, we illustrate that the spatial autocorrelation measured by Geary’s c is positive (resp. negative) if spatially smoother (resp. less smooth) graph Laplacian eigenvectors are dominant. Finally, based on our analysis, we provide a recommendation for applied studies.

Interpretable machine learning for analysing heterogeneous drivers of geographic events in space-time

ABSTRACT Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodologically aspatial ML algorithms with an apparent high predictive power ignore non-stationary domain relationships in spatio-temporal data (e.g. dependence, heterogeneity), leading to incorrect interpretations and poor management decisions. This study addresses this critical methodological issue of ‘interpretability’ in ML-based modeling of structural relationships using the example of heterogeneous drivers of wildfires across the United States. Specifically, we present and evaluate a spatio-temporally interpretable random forest (iST-RF) that uses spatio-temporal sampling-based training and weighted prediction. Although the ultimate scientific objective is to derive interpretation in space-time, experiments show that iST-RF can improve predictive accuracy (76%) compared to the aspatial RF approach (70%) while enhancing interpretations of the trained model’s spatio-temporal relevance for its ensemble prediction. This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist for predictive modeling when the dataset is very small because in such cases locally optimized sub-model’s prediction performance can be suboptimal. With that caveat, our proposed approach is an ideal choice for identifying drivers of spatio-temporal events at country- or regional-scale studies.

Transdisziplinäre Realexperimente und künstlerische Forschungspraktiken. Koproduktion urbaner Resilienz im Reallabor Gängeviertel in Hamburg

Using real-world laboratories, researchers in spatial sciences increasingly engage in both, shaping and observing transformation processes of urban systems. Thereby, real-world experiments are critical. As interventions into the real, they serve to test and foster possibilities of change. This paper discusses two real-world experiments with reference to the Gängeviertel in Hamburg. Their purpose was to shape the conflictual cooperation process to renovate the historic inner city quarter in a more efficient way and to gain knowledge about motives and values of those who are involved. The paper focusses on arts-based practices such as staging, arranging and performing, that were integrated into the research design with the purpose to outline their potentials for transformative research as well as to discuss challenges and constraints. Furthermore the scientific assumptions of the research design are illustrated.

Advancing multimedia learning for science: Comparing the effect of virtual versus physical models on student learning about stereochemistry

AbstractEmbodied learning tools (ELTs), such as building and manipulating models, bolster students’ learning of spatially demanding science content, such as stereochemistry. However, studies comparing the effectiveness of virtual to physical ELTs on student learning are limited. This study compares online instruction using a virtual ELT, virtual models, to a physical ELT, physical models, on organic chemistry students' stereochemistry understanding. Furthermore, it identifies which tool promotes the transfer and application of spatial science content to higher order concepts, such as reaction mechanisms. Lastly, the role of students' spatial skills is considered. To assess the magnitude of the effect of each tool, two control groups were included, a text‐based instruction and a no instruction group. Results revealed that the physical model group showed greater stereochemistry understanding compared to the no instruction group, while there was no difference between the virtual model and the no instruction groups. When asked to apply their knowledge to reasoning about reaction mechanisms, the physical model group outperformed the virtual model group and both control groups. Students' spatial skills did not influence their understanding of either concept in any learning condition. These results suggest that regardless of spatial skills, physical ELTs may provide the necessary scaffolding for students to learn spatially demanding science content, whereas virtual ELTs are no more effective than receiving no formal instruction. Furthermore, physical ELTs may be more effective than virtual ELTs in facilitating the application of that knowledge to more complex concepts. These findings have broad implications for online science pedagogy.

A review of GIS methodologies to analyze the dynamics of COVID-19 in the second half of 2020.

COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.

Assessing emergency healthcare accessibility in the Salton Sea region of Imperial County, California.

The area surrounding California's Salton Sea, which lies within Riverside and Imperial counties, has particularly negative health outcomes. Imperial County, a primarily rural region that encompasses the lake, has pediatric asthma-related emergency healthcare visits that double the state average. This paper seeks to assess the level of emergency healthcare access in the Salton Sea region of Imperial County, drawing from spatial science methods. For this study, the "Salton Sea region" is defined as all Imperial County census tracts that include the Salton Sea within its boundaries. To measure "access," this study calculated driving travel times from census tracts to hospitals within Imperial County rather than Euclidean distance to account for geography and urban infrastructures such as road networks and traffic conditions. This study also used the Rational Agent Access Model, or RAAM, to assess access. RAAM scores account for the supply and demand for hospitals in addition to travel times. Results showed that the average travel time for Salton Sea region residents to drive to Imperial County emergency healthcare facilities ranged from 50-61 minutes, compared to 14-20 minutes for other Imperial County tracts. RAAM scores, compared to other Imperial County tracts, were about 30% higher in the Salton Sea region, meaning that healthcare supply is limited in the region. State and county policy should account for spatial inaccessibility to healthcare institutions in order to address emergency healthcare access.

Follow the Firm: Analyzing the International Ascendance of Build to Rent

abstract Economic geography and housing studies have begun to grapple with how institutional investment operates and impacts particular cities or sites. There has been less attention to the ways in which institutional investment functions across different scales: the theorization of financialization has, to date, left unaddressed the ways in which global or international actors confront multiple scales of corporate strategy, politics, and markets. In this article we utilize a firm-level analysis to engage with the financialization of rental housing in two cities, by following a residential landlord’s entrance into professionalized private residential rental—build to rent (BTR)—markets in London and Amsterdam. Conceptually, we develop a firm-centered approach for analyzing the multiscalar nature of financing BTR to rent housing. This approach combines valuable insights from work on the firm in economic geography and critical perspectives from the wider spatial sciences, to fully grasp a firm’s behavior in relation to the wider political institutional dynamics. We reveal how the firm’s multiscalar corporate strategies interact with highly territorialized systems of regulation and governance in both cities. Despite different market dynamics in London and Amsterdam, in both cities the firm mitigates risk by entering the market via student housing; it acquires local knowledge, and it establishes meaningful connections with private local actors and policy makers. These insights, we contend, contribute to the theorization of financialization by demonstrating the multiscalar nature of the processes embedded within it.

Seeing China’s social-cultural change through the eyes of tourists

ABSTRACT Our study scrutinises the personal and collective nostalgia that shapes the performativity of tourists, in order to analyse how tourism provides a space through which urban tourists understand rural China. By interrogating the social-cultural arena through which tourists experience the countryside, we examine how toured places are imagined and consumed. Our main purpose is to ponder, through the lens of tourism, the social-cultural change post-China’s economic reform that was started in 1978. We utilise reflexive ethnography to delineate the experiences of urban tourists, in light of their perceptions of the rural places. Our findings suggest that the past is remembered in terms of the grand narratives of a bygone era of socio-political coherence, which the tourists feel has been eroded against the changing urban landscape. Methodologically, we aim to explore the possibility of pushing tourism enquiries further, from a predominantly positivist spatial science that seeks objective facts, to a more humanistic field of research involving existential and phenomenological methods.

Developing a flexible learning activity on biodiversity and spatial scale concepts using open-access vegetation datasets from the National Ecological Observatory Network.

Biodiversity is a complex, yet essential, concept for undergraduate students in ecology and other natural sciences to grasp. As beginner scientists, students must learn to recognize, describe, and interpret patterns of biodiversity across various spatial scales and understand their relationships with ecological processes and human influences. It is also increasingly important for undergraduate programs in ecology and related disciplines to provide students with experiences working with large ecological datasets to develop students’ data science skills and their ability to consider how ecological processes that operate at broader spatial scales (macroscale) affect local ecosystems. To support the goals of improving student understanding of macroscale ecology and biodiversity at multiple spatial scales, we formed an interdisciplinary team that included grant personnel, scientists, and faculty from ecology and spatial sciences to design a flexible learning activity to teach macroscale biodiversity concepts using large datasets from the National Ecological Observatory Network (NEON). We piloted this learning activity in six courses enrolling a total of 109 students, ranging from midlevel ecology and GIS/remote sensing courses, to upper‐level conservation biology. Using our classroom experiences and a pre/postassessment framework, we evaluated whether our learning activity resulted in increased student understanding of macroscale ecology and biodiversity concepts and increased familiarity with analysis techniques, software programs, and large spatio‐ecological datasets. Overall, results suggest that our learning activity improved student understanding of biological diversity, biodiversity metrics, and patterns of biodiversity across several spatial scales. Participating faculty reflected on what went well and what would benefit from changes, and we offer suggestions for implementation of the learning activity based on this feedback. This learning activity introduced students to macroscale ecology and built student skills in working with big data (i.e., large datasets) and performing basic quantitative analyses, skills that are essential for the next generation of ecologists.

La trajectographie, une proposition pour capturer les outils de gestion

The trajectography, a proposal for capturing management toolsThis article proposes to reconcile two approaches traditionally used to study management tools (content and process) by mobilising the concept of trajectography. Initially used in the spatial sciences, we propose its conceptualisation and a reading grid adapted to the management sciences. This research is based on a research-intervention that took place within a professional rugby sports organisation (PROD7) over a period of eighteen months. The results of the research highlight the contributions of the transfer of the concept of trajectography. The latter makes it possible, on the one hand, to propose a global vision of the processes of deployment of management tools and, on the other hand, to apprehend the organisational issues peripheral to these management situations.

Geospatial Techniques Contribution for Climate Change Adaptation: Review of Literatures on Ethiopia

Ethiopia is highly susceptible to the global climate change due to its fragile environment and nature of its economic base. To this effect, climate change adaptation strengthened by geospatial technologies is vital. This paper is, therefore, intended to assess the role of geospatial techniques in climate change adaptation activities. Various research articles, reports and books were reviewed. The surveyed literatures showed that geospatial techniques can help climate change adaptation either by identifying potential capabilities or estimating upcoming risks. When socio-economic and ecological vulnerabilities are assessed, it enables to alarm for the necessity of climate change adaptation, and when socio-economic capacity and natural resources endowment are clearly identified and mapped it augments climate change adaptation. That is, geospatial techniques have been utilized in various areas in order to generate geographic, climatic, geologic, biological and geo-referenced socio-economic situations of different parts of Ethiopia. In addition, there have been increased numbers of researches conducted using geospatial analysis methods that obtained opportunities from the advancement and suitability of the field. To conclude, integration of geospatial techniques enables to move toward solution of climate change problem which is geographical in its nature. Thus, all stakeholders should contribute for advancement of spatial science and have to develop the habit of applying the findings obtained from researches conducted using geospatial techniques.

From spatial to platial - the role and future of immersive technologies in the spatial sciences

Immersive technologies such as virtual and augmented reality have been part of the technology mindset in computer and geospatial sciences early on. The promise of delivering realistic experiences to the human senses that are not bound by physical reality has inspired generations of scientists and entrepreneurs alike. However, the vision for immersive experiences has been in stark contrast to the ability to deliver at the technology end; the community has battled nuisances such as cybersickness, tethers, and the uncanny valley for the last decades. With the 'final wave' of immersive technologies, we are now able to fulfill a long-held promise and freely and creatively envision how immersive technologies change spatial sciences by creating embodied experiences for geospatial applications. These experiences are not restricted by time or place, nor are they limited to the physical world. This contribution envisions the future of spatial sciences in light of place-like experiences enabled through immersive technologies and their potential to infuse research in the spatial sciences community.

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (Gi4DM2020): PREFACE

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (Gi4DM2020): PREFACE

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (GI4DM2020): PREFACE

GEOINFORMATION FOR DISASTER MANAGEMENT 2020 (GI4DM2020): PREFACE

On the interpretability of predictors in spatial data science: the information horizon

Two important theories in spatial modelling relate to structural and spatial dependence. Structural dependence refers to environmental state-factor models, where an environmental property is modelled as a function of the states and interactions of environmental predictors, such as climate, parent material or relief. Commonly, the functions are regression or supervised classification algorithms. Spatial dependence is present in most environmental properties and forms the basis for spatial interpolation and geostatistics. In machine learning, modelling with geographic coordinates or Euclidean distance fields, which resemble linear variograms with infinite ranges, can produce similar interpolations. Interpolations do not lend themselves to causal interpretations. Conversely, with structural modeling, one can, potentially, extract knowledge from the modelling. Two important characteristics of such interpretable environmental modelling are scale and information content. Scale is relevant because very coarse scale predictors can show nearly infinite ranges, falling out of what we call the information horizon, i.e. interpretation using domain knowledge isn’t possible. Regarding information content, recent studies have shown that meaningless predictors, such as paintings or photographs of faces, can be used for spatial environmental modelling of ecological and soil properties, with accurate evaluation statistics. Here, we examine under which conditions modelling with such predictors can lead to accurate statistics and whether an information horizon can be derived for scale and information content.

Editorial for the special issue: “Novel Solutions and Novel Approaches in Operational Research”

Abstract This special issue of Business Systems Research (SI of the BSR) is co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI-SOR) and highlights recent advances in Operations Research and Management Science (OR /MS), with a focus on linking OR /MS with other areas of quantitative and qualitative methods in a multidisciplinary framework. Eleven papers selected for this SI of the BSR present improvements and new techniques (methodology) in Operations Research (OR) and their application in various fields of economics, business, spatial science, smart mobility, higher education, human resources, environment, agriculture and social networks.

The Right Tools for the Job: The Case for Spatial Science Tool-Building

This paper was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze non-planar urban spatial networks have traditionally been inaccessible, a theoretical, or otherwise limiting. This paper reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open‐source software development. Tool-building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The paper concludes with paths forward, emphasizing open-source software and reusable computational data science beyond mere reproducibility and replicability.

The right tools for the job: The case for spatial science tool‐building

AbstractThis article was presented as the 8th annual Transactions in GIS plenary address at the American Association of Geographers annual meeting in Washington, DC. The spatial sciences have recently seen growing calls for more accessible software and tools that better embody geographic science and theory. Urban spatial network science offers one clear opportunity: from multiple perspectives, tools to model and analyze non‐planar urban spatial networks have traditionally been inaccessible, atheoretical, or otherwise limiting. This article reflects on this state of the field. Then it discusses the motivation, experience, and outcomes of developing OSMnx, a tool intended to help address this. Next it reviews this tool's use in the recent multidisciplinary spatial network science literature to highlight upstream and downstream benefits of open‐source software development. Tool‐building is an essential but poorly incentivized component of academic geography and social science more broadly. To conduct better science, we need to build better tools. The article concludes with paths forward, emphasizing open‐source software and reusable computational data science beyond mere reproducibility and replicability.

Developing Lesson Plans for Teaching Spatial Data Management in Academic Libraries through a Lens of Popular Culture

ABSTRACT Understanding data management in spatial data science is fundamentally important when ensuring a project’s data is appropriately collected, analyzed, stored, and communicated. However, these concepts are often introduced to learners through compliance (i.e., we do this because we are required to or it is the right thing to do), and as a result can mean learning experiences around spatial data management are viewed less than enthusiastically. Academic libraries are places where engaging learning experiences around spatial data management can be explored and implemented through unique pedagogical techniques. For example, activities teaching spatial data management in academic libraries through a popular culture lens can make these topics more approachable and reduce learning barriers. In this paper, I present three lesson plans for academic librarians which approach teaching spatial data management through popular culture. The following concepts are represented: (1) understanding spatial metadata through Dungeons & Dragons; (2) learning to write good documentation through drawing Pokémon; and (3) developing good file naming habits with 1980s album covers. Alongside the lesson plans, I provide a narrative outlining why popular culture pedagogy is a useful technique to spatial data management education to increase the inclusivity and engagement around geospatial learning experiences in academic libraries.