Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.296
Alan T. Murray, Jiwon Baik, Hannah Malak
GIScience and spatial information contributions to indoor mapping and navigation are many, but there remain significant challenges. Indoor environments are where people spend most of their time, socializing, working, learning, exercising, etc. During times of emergencies, disease outbreaks, and crises, indoor management and planning must be prepared to handle such events, yet doing so is often hindered by a lack of supporting spatial information and appropriate analytics. This paper focuses on COVID-19 mitigation measures to reduce disease transmission through physical distancing in indoor spaces, such as classrooms, offices, dining commons, restaurants, and entertainment venues. Geographical data to support indoor environments is discussed, particularly issues of acquisition, spatial data uncertainty, and implications for spatial analytics. Planning for classroom physical distancing on a university campus highlights capabilities, issues, and challenges, with a comparison made between previous studies relying on architectural data and more precise information obtained using LiDAR.
{"title":"Assessing the influence of indoor mapping sources for indoor spatial analysis of physical distancing","authors":"Alan T. Murray, Jiwon Baik, Hannah Malak","doi":"10.5311/josis.2023.27.296","DOIUrl":"https://doi.org/10.5311/josis.2023.27.296","url":null,"abstract":"GIScience and spatial information contributions to indoor mapping and navigation are many, but there remain significant challenges. Indoor environments are where people spend most of their time, socializing, working, learning, exercising, etc. During times of emergencies, disease outbreaks, and crises, indoor management and planning must be prepared to handle such events, yet doing so is often hindered by a lack of supporting spatial information and appropriate analytics. This paper focuses on COVID-19 mitigation measures to reduce disease transmission through physical distancing in indoor spaces, such as classrooms, offices, dining commons, restaurants, and entertainment venues. Geographical data to support indoor environments is discussed, particularly issues of acquisition, spatial data uncertainty, and implications for spatial analytics. Planning for classroom physical distancing on a university campus highlights capabilities, issues, and challenges, with a comparison made between previous studies relying on architectural data and more precise information obtained using LiDAR. ","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"69 10","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.246
Kirsty Watkinson, Jonathan J. Huck, Angela Harris
As a consequence of their reliance on a scarce volunteer resource, humanitarian mapping organizations must prioritize their mapping activities. For mapping in anticipation of a crisis or mapping in support of long-term crises, the only method available to organizations is an estimation of the "completeness" of the map, with organizations directing volunteers to map areas where data are missing. Whilst this method is suitable for organizations that focus on general map improvement, for those who create data for a specific reason (e.g., drinking water provision) the method is sub-optimal. In this article, we present a new method of humanitarian mapping prioritization, that considers the purpose of map data collection. The method identifies locations where contributions by volunteers are expected to have the biggest impact on the desired use of the map data and therefore maximizes the value gained from volunteer contributions. We explain our method using the example of measuring distance to healthcare and demonstrate its superior ability to consider the context of map data over generic estimations of map "completeness". Our method provides humanitarian mapping organizations with an easily reproducible and low cost method and an opportunity to make better informed decisions about mapping prioritization, when the purpose of map data collection is known. Using our method, organizations will be able to maximize the value gained from a scarce volunteer resource and increase the efficiency of humanitarian map data production.
{"title":"Maximizing the value of a volunteer: A novel method for prioritizing humanitarian VGI activities","authors":"Kirsty Watkinson, Jonathan J. Huck, Angela Harris","doi":"10.5311/josis.2023.27.246","DOIUrl":"https://doi.org/10.5311/josis.2023.27.246","url":null,"abstract":"As a consequence of their reliance on a scarce volunteer resource, humanitarian mapping organizations must prioritize their mapping activities. For mapping in anticipation of a crisis or mapping in support of long-term crises, the only method available to organizations is an estimation of the \"completeness\" of the map, with organizations directing volunteers to map areas where data are missing. Whilst this method is suitable for organizations that focus on general map improvement, for those who create data for a specific reason (e.g., drinking water provision) the method is sub-optimal. In this article, we present a new method of humanitarian mapping prioritization, that considers the purpose of map data collection. The method identifies locations where contributions by volunteers are expected to have the biggest impact on the desired use of the map data and therefore maximizes the value gained from volunteer contributions. We explain our method using the example of measuring distance to healthcare and demonstrate its superior ability to consider the context of map data over generic estimations of map \"completeness\". Our method provides humanitarian mapping organizations with an easily reproducible and low cost method and an opportunity to make better informed decisions about mapping prioritization, when the purpose of map data collection is known. Using our method, organizations will be able to maximize the value gained from a scarce volunteer resource and increase the efficiency of humanitarian map data production. ","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"40 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138948119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.268
Niels Steenbergen, Eric Top, E. Nyamsuren, Simon Scheider
Transformations are essential for dealing with geographic information. They are involved not only in the conversion between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relation types, and a set of basic transformations described in terms of function signatures that use such types. We enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions and inferring goal concepts across a workflow. In this article, we show how such procedural metadata can be used to retrieve workflows based on task descriptions derived from geo-analytical questions. Transformations can be queried independently from their implementations or data formats.
{"title":"Procedural metadata for geographic information using an algebra of core concept transformations","authors":"Niels Steenbergen, Eric Top, E. Nyamsuren, Simon Scheider","doi":"10.5311/josis.2023.27.268","DOIUrl":"https://doi.org/10.5311/josis.2023.27.268","url":null,"abstract":"Transformations are essential for dealing with geographic information. They are involved not only in the conversion between geodata formats and reference systems, but also in turning geodata into useful information according to some purpose. However, since a transformation can be implemented in various formats and tools, its function and purpose usually remains hidden underneath the technicalities of a workflow. To automate geographic information procedures, we therefore need to model the transformations implemented by workflows on a conceptual level, as a form of procedural knowledge. Although core concepts of spatial information provide a useful level of description in this respect, we currently lack a model for the space of possible transformations between such concepts. In this article, we present the algebra of core concept transformations (CCT). It consists of a type hierarchy which models core concepts as relation types, and a set of basic transformations described in terms of function signatures that use such types. We enrich GIS workflows with abstract machine-readable metadata, by compiling algebraic tool descriptions and inferring goal concepts across a workflow. In this article, we show how such procedural metadata can be used to retrieve workflows based on task descriptions derived from geo-analytical questions. Transformations can be queried independently from their implementations or data formats.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"54 8","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138950859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.277
Marion Dumont, G. Touya, Cécile Duchêne
When you zoom in or out of current multi-scale cartographic applications, it is common to feel lost and disoriented for a few seconds because dimensions and map symbols have changed. To make the exploration of these multi-scale maps more fluid, one option is to design maps where the transformations due to scale change are more progressive. This paper proposes to use cartographic generalization techniques to design these multi-scale maps with additional intermediate scales to improve progressiveness. These more progressive maps are tested in a user study with a task requiring multiple zooms in and out. The users perform better with the progressive maps, and in particular, the total quantity of required zooming is reduced compared to maps without additional intermediate scales. However, the survey is not fully conclusive on task performance due to the complexity of such a survey with real maps. This difficulty in assessing how well progressive map generalisation reduces disorientation is discussed and guidelines are proposed to design further studies.
{"title":"More is less - Adding zoom levels in multi-scale maps to reduce the need for zooming interactions","authors":"Marion Dumont, G. Touya, Cécile Duchêne","doi":"10.5311/josis.2023.27.277","DOIUrl":"https://doi.org/10.5311/josis.2023.27.277","url":null,"abstract":"When you zoom in or out of current multi-scale cartographic applications, it is common to feel lost and disoriented for a few seconds because dimensions and map symbols have changed. To make the exploration of these multi-scale maps more fluid, one option is to design maps where the transformations due to scale change are more progressive. This paper proposes to use cartographic generalization techniques to design these multi-scale maps with additional intermediate scales to improve progressiveness. These more progressive maps are tested in a user study with a task requiring multiple zooms in and out. The users perform better with the progressive maps, and in particular, the total quantity of required zooming is reduced compared to maps without additional intermediate scales. However, the survey is not fully conclusive on task performance due to the complexity of such a survey with real maps. This difficulty in assessing how well progressive map generalisation reduces disorientation is discussed and guidelines are proposed to design further studies. ","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"31 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.220
Majid Hojati, Rob Feick, Steven Roberts, Carson Farmer, Colin Robertson
With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.
随着新技术的出现和地理空间数据生产的广泛参与，空间数据共享面临着新的挑战。空间数据共享实践越来越多地通过少数私人控制的企业服务进行交易，并在不同程度上受其控制。数据生产已从主要是集中式、专家导向型和权威性的，发展到现在还包括混合数据收集过程，涉及个人的分布式集合，他们在通过集中式架构和控制制度进行互动的同时，共享和共同生产空间数据。这些变化主要源于与 Web 2.0 和广泛使用的互联网参与工具的出现相关的技术和社会变革。由于人们对空间数据访问和共享的控制方式，尤其是敏感数据或个人身份数据的控制方式感到担忧，因此人们对允许用户独立于集中式平台共享资源的分布式文件技术越来越感兴趣。本文探讨了随着分布式网络技术的进一步成熟，空间数据共享实践如何向更加分散的共享生态系统转变。我们认为，随着数据所有权和访问控制问题的日益混合形式与新的分布式系统（如 Web 3.0）相结合，这种转变也会发生。我们还讨论了分布式空间数据共享的机遇和障碍，包括对大型地理数据管理可能带来的好处，以及需要制定协议来共享、整合和处理分布式网络上共享的空间数据。
{"title":"Distributed spatial data sharing: a new model for data ownership and access control","authors":"Majid Hojati, Rob Feick, Steven Roberts, Carson Farmer, Colin Robertson","doi":"10.5311/josis.2023.27.220","DOIUrl":"https://doi.org/10.5311/josis.2023.27.220","url":null,"abstract":"With the advent of new technologies and broader participation in geospatial data production, new challenges emerge for spatial data sharing. Spatial data sharing practices are increasingly transacted through and, to varying degrees, controlled by a handful of privately controlled corporate services. Data production has evolved from being largely centralized, expert-oriented, and authoritative in nature to now also include hybrid data collection processes involving distributed assemblages of individuals who share and co-produce spatial data while interacting through centralized architectures and control regimes. These changes have resulted mainly from technological and social changes linked to the emergence of Web 2.0 and widely available Internet participation tools. Concerns about how spatial data access and sharing are controlled, particularly for sensitive or personally-identifying data, have increased interest in distributed file technologies that allow users to share resources independently of centralized platforms. This paper examines how spatial data sharing practices may move towards a more decentralized sharing ecosystem as technologies for a further distributed web mature. We identify this transition as increasingly hybridized forms of data ownership and access control concerns are coupled with new distributed systems (e.g., Web 3.0). We also discuss opportunities and barriers to distributed spatial data sharing, including possible benefits for big geographic data management and the need for protocols to share, integrate, and process spatial data shared on distributed networks.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"38 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138948122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.5311/josis.2023.27.307
Hongyu Zhang
{"title":"Reimagining GIScience education for enhanced employability","authors":"Hongyu Zhang","doi":"10.5311/josis.2023.27.307","DOIUrl":"https://doi.org/10.5311/josis.2023.27.307","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"39 4","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138949794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.5311/josis.2023.26.305
Benjamin Adams, S. Dodge, R. Purves
Since our 10th anniversary issues published in 2020 [1, 2], JOSIS has continued to publish a number of excellent research articles on many of the topics highlighted by our editorial board in their invited papers. These include articles on crowdsourcing [10], place [18, 17, 23, 13, 3], spatial language [5, 15, 24, 19], GeoAI [14], movement analysis [26, 11], urban analysis and wayfinding [16, 21, 7], methods for spatial analysis and uncertainty [22, 25, 6, 20], environmental data and modeling [8, 12, 9], and qualitative spatial reasoning [4]. We are happy to also note that these articles represent research conducted around the world, with authors based in Australia, Bangladesh, Canada, Croatia, France, Germany, Lebanon, the Netherlands, New Zealand, Portugal, South Africa, Switzerland, the United Kingdom, and the United States. In addition to five research articles, this issue contains two commentaries that revisit thematic questions about the field of GIScience both past and present. The first of these by René Westerholt examines how GIScience is taught in the interdisciplinary contexts and how that affects the identity of the field. The second by Christophe Claramunt and Matthew Dube looks at the state of the field through the lens of the original NCGIA research agenda. As we go forward, it is clear that spatial information science (and GIScience) continues to evolve as both a scientific field as well as in terms of the applications to which it is applied. We take this opportunity to remind all of our readers that JOSIS is run by your researchers for researchers. As a diamond open access journal, your article will be published under a Creative Commons licence, with no fees to either readers or authors. We rely on the community to provide constructive and detailed reviews, and are proud of the quality and diversity of articles we publish.
{"title":"Spatial Information Science in 2023","authors":"Benjamin Adams, S. Dodge, R. Purves","doi":"10.5311/josis.2023.26.305","DOIUrl":"https://doi.org/10.5311/josis.2023.26.305","url":null,"abstract":"Since our 10th anniversary issues published in 2020 [1, 2], JOSIS has continued to publish a number of excellent research articles on many of the topics highlighted by our editorial board in their invited papers. These include articles on crowdsourcing [10], place [18, 17, 23, 13, 3], spatial language [5, 15, 24, 19], GeoAI [14], movement analysis [26, 11], urban analysis and wayfinding [16, 21, 7], methods for spatial analysis and uncertainty [22, 25, 6, 20], environmental data and modeling [8, 12, 9], and qualitative spatial reasoning [4]. We are happy to also note that these articles represent research conducted around the world, with authors based in Australia, Bangladesh, Canada, Croatia, France, Germany, Lebanon, the Netherlands, New Zealand, Portugal, South Africa, Switzerland, the United Kingdom, and the United States. In addition to five research articles, this issue contains two commentaries that revisit thematic questions about the field of GIScience both past and present. The first of these by René Westerholt examines how GIScience is taught in the interdisciplinary contexts and how that affects the identity of the field. The second by Christophe Claramunt and Matthew Dube looks at the state of the field through the lens of the original NCGIA research agenda. As we go forward, it is clear that spatial information science (and GIScience) continues to evolve as both a scientific field as well as in terms of the applications to which it is applied. We take this opportunity to remind all of our readers that JOSIS is run by your researchers for researchers. As a diamond open access journal, your article will be published under a Creative Commons licence, with no fees to either readers or authors. We rely on the community to provide constructive and detailed reviews, and are proud of the quality and diversity of articles we publish.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46268910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.5311/josis.2023.26.300
Christophe Claramunt, Matthew P. Dube
Geographical information science (GIScience) is progressively acknowledged as a scientific field based on a wide range of theories and methods that are constantly evolving. This motivates our attempt at a tentative observation of the research progress and challenges that have gone along with its gradual recognition as a domain of its own. The brief critical review presented in this paper develops an observation of such evolution. The peculiarity of our approach is that it is not based on a quantitative evaluation of the research outputs as identified by usual journal production metrics, but rather on a progressive identification of the research questions and their evolution, which the GIS academic community has been addressing over the past 30 years since the landmark NCGIA initiatives' research agendas have largely inspired and contributed to the development of geographical information science as a field.
{"title":"A brief review of the evolution of GIScience since the NCGIA research agenda initiatives","authors":"Christophe Claramunt, Matthew P. Dube","doi":"10.5311/josis.2023.26.300","DOIUrl":"https://doi.org/10.5311/josis.2023.26.300","url":null,"abstract":"Geographical information science (GIScience) is progressively acknowledged as a scientific field based on a wide range of theories and methods that are constantly evolving. This motivates our attempt at a tentative observation of the research progress and challenges that have gone along with its gradual recognition as a domain of its own. The brief critical review presented in this paper develops an observation of such evolution. The peculiarity of our approach is that it is not based on a quantitative evaluation of the research outputs as identified by usual journal production metrics, but rather on a progressive identification of the research questions and their evolution, which the GIS academic community has been addressing over the past 30 years since the landmark NCGIA initiatives' research agendas have largely inspired and contributed to the development of geographical information science as a field.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41665722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-30DOI: 10.5311/josis.2023.26.299
R. Westerholt
Many GIScientists are affiliated with institutions beyond what we would call core GIScience. This implies that we teach in degree programs that follow their own curricular logic, and presents us with challenges in terms of what to teach, how to possibly attract students to GIScience careers, and in terms of our own self-images and identities. After briefly taking stock of some of the bigger curricular initiatives from the past 30 years, and informed by a brief discussion of key arguments and findings regarding teaching GIScience 'elsewhere', this commentary aims to stimulate discussion on the multifaceted and multidisciplinary nexus in which many of us are embedded. The commentary includes short reflections on the implications of the multidisciplinary contexts mentioned for the creation of a GIScience identity among students enrolled in other degrees, recruitment of PhD students and faculty, and what all this possibly means for how we see ourselves as GIScientists.
{"title":"Teaching GIScience in the multidisciplinary nexus","authors":"R. Westerholt","doi":"10.5311/josis.2023.26.299","DOIUrl":"https://doi.org/10.5311/josis.2023.26.299","url":null,"abstract":"Many GIScientists are affiliated with institutions beyond what we would call core GIScience. This implies that we teach in degree programs that follow their own curricular logic, and presents us with challenges in terms of what to teach, how to possibly attract students to GIScience careers, and in terms of our own self-images and identities. After briefly taking stock of some of the bigger curricular initiatives from the past 30 years, and informed by a brief discussion of key arguments and findings regarding teaching GIScience 'elsewhere', this commentary aims to stimulate discussion on the multifaceted and multidisciplinary nexus in which many of us are embedded. The commentary includes short reflections on the implications of the multidisciplinary contexts mentioned for the creation of a GIScience identity among students enrolled in other degrees, recruitment of PhD students and faculty, and what all this possibly means for how we see ourselves as GIScientists.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42067800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.5311/josis.2023.26.212
M. Sachdeva, A. Fotheringham
The concept of scale is inherent to, and consequential for, the modeling of geographical processes. However, scale also causes huge problems because the results of many types of spatial analysis appear to be dependent on the scale of the units for which data are reported (measurement scale). Consequently, when the same spatial models are calibrated at different scales of aggregations, the results are often vastly different (the well-known Modifiable Areal Unit Problem or MAUP). With the advent of local models and the fundamental difference in their scale of application compared to global models, this issue is further exacerbated in unexpected ways. For example, a global model and local model calibrated using data measured at the same aggregation scale can also result in different and sometimes contradictory inferences (the classic Simpson's Paradox). Here we provide a geographical perspective on why and how contrasting inferences might result from the calibration of a local and global model using the same data. Further, we examine the viability of such an occurrence using a synthetic experiment and two empirical examples. Finally, we discuss how such a perspective might inform the analyst’s conundrum: when the respective inferences run counter to one another, do we believe the local or global model results?
{"title":"A Geographical Perspective on Simpson's Paradox","authors":"M. Sachdeva, A. Fotheringham","doi":"10.5311/josis.2023.26.212","DOIUrl":"https://doi.org/10.5311/josis.2023.26.212","url":null,"abstract":"The concept of scale is inherent to, and consequential for, the modeling of geographical processes. However, scale also causes huge problems because the results of many types of spatial analysis appear to be dependent on the scale of the units for which data are reported (measurement scale). Consequently, when the same spatial models are calibrated at different scales of aggregations, the results are often vastly different (the well-known Modifiable Areal Unit Problem or MAUP). With the advent of local models and the fundamental difference in their scale of application compared to global models, this issue is further exacerbated in unexpected ways. For example, a global model and local model calibrated using data measured at the same aggregation scale can also result in different and sometimes contradictory inferences (the classic Simpson's Paradox). Here we provide a geographical perspective on why and how contrasting inferences might result from the calibration of a local and global model using the same data. Further, we examine the viability of such an occurrence using a synthetic experiment and two empirical examples. Finally, we discuss how such a perspective might inform the analyst’s conundrum: when the respective inferences run counter to one another, do we believe the local or global model results?","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46686529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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