Pub Date : 2022-01-16DOI: 10.1080/19475683.2022.2026477
F. Mahuve, B. Tarimo
ABSTRACT The use of discrete (binary or zonal/step-wise) instead of continuous travel impedance functions in floating catchment area (FCA) measures generates inconsistent and less reliable accessibility indices that might be overestimating or underestimating actual accessibility. Thus, this study illustrates the limitations of discrete travel impedance function in the enhanced two-step (E2S) FCA method; and develops a fuzzy-based continuous travel impedance function by combining the degree to which travel distances belong to different sub-zones of catchment areas and their respective E2SFCA zonal weights using the weighted mean fuzzy-set operation. With the developed fuzzy-based travel impedance function, a new accessibility measure, fuzzy-based two-step (F2S) FCA is defined. The E2SFCA and F2SFCA measures were implemented in the hypothetical set up and Rural Wards of Dodoma Urban District in Tanzania to determine spatial accessibility of service points and water points, respectively. The resulting E2SFCA and F2SFCA accessibility indices portrayed a slightly similar pattern. However, F2SFCA accessibility indices varied among households within and across sub-zones, while E2SFCA accessibility indices were identical within but changed abruptly across sub-zones. The variations in F2SFCA accessibility indices reflect the reality and to a greater extent Tobler’s first law of Geography. Thus, the developed F2SFCA measure could be used to measure spatial accessibility of other services or commodities as it properly models travel impedance. The F2SFCA measure could further be improved to generate much more realistic accessibility indices by capturing local instead of total potential demand, commonly modelled in FCA measures.
{"title":"Integrating fuzzy set function into floating catchment area measures: a determination of spatial accessibility of service points","authors":"F. Mahuve, B. Tarimo","doi":"10.1080/19475683.2022.2026477","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026477","url":null,"abstract":"ABSTRACT The use of discrete (binary or zonal/step-wise) instead of continuous travel impedance functions in floating catchment area (FCA) measures generates inconsistent and less reliable accessibility indices that might be overestimating or underestimating actual accessibility. Thus, this study illustrates the limitations of discrete travel impedance function in the enhanced two-step (E2S) FCA method; and develops a fuzzy-based continuous travel impedance function by combining the degree to which travel distances belong to different sub-zones of catchment areas and their respective E2SFCA zonal weights using the weighted mean fuzzy-set operation. With the developed fuzzy-based travel impedance function, a new accessibility measure, fuzzy-based two-step (F2S) FCA is defined. The E2SFCA and F2SFCA measures were implemented in the hypothetical set up and Rural Wards of Dodoma Urban District in Tanzania to determine spatial accessibility of service points and water points, respectively. The resulting E2SFCA and F2SFCA accessibility indices portrayed a slightly similar pattern. However, F2SFCA accessibility indices varied among households within and across sub-zones, while E2SFCA accessibility indices were identical within but changed abruptly across sub-zones. The variations in F2SFCA accessibility indices reflect the reality and to a greater extent Tobler’s first law of Geography. Thus, the developed F2SFCA measure could be used to measure spatial accessibility of other services or commodities as it properly models travel impedance. The F2SFCA measure could further be improved to generate much more realistic accessibility indices by capturing local instead of total potential demand, commonly modelled in FCA measures.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"58 1","pages":"307 - 323"},"PeriodicalIF":5.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75477412","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 : 2022-01-16DOI: 10.1080/19475683.2022.2026474
Kyusik Kim, M. Ghorbanzadeh, M. Horner, E. Ozguven
ABSTRACT Community-wide vaccinations would be the most effective way to end the COVID-19 pandemic, and accessing vaccination sites would be central in this nexus. Given that the number of COVID-19 vaccines was limited to certain groups of people in the early phases of vaccine distribution, age-based prioritization may have overlooked differences in income levels and the races/ethnicities among older populations. In this vein, using two spatial accessibility measures based on spatially disaggregated hexagons, this paper assesses the disparities in spatial accessibility to vaccination sites with consideration of older populations’ (65+) income levels and their races/ethnicities at the state and the county level. To evaluate the disparities and identify counties with the greatest disparities, a non-parametric two-sample Kolmogorov–Smirnov test at the state level and the statistic at the county level are implemented. The findings of this study indicate that older blacks, older Hispanics, and older populations below the poverty level had better access compared to older whites, older non-Hispanics, and older populations above the poverty level, respectively, at the state level, whereas access disparities varied at counties and geographic locales. We thus conclude that policymakers should take into account older populations’ income levels and races/ethnicities for vaccine prioritization and should pay attention to counties with relatively high disparities in spatial access to vaccines.
{"title":"Assessment of disparities in spatial accessibility to vaccination sites in Florida","authors":"Kyusik Kim, M. Ghorbanzadeh, M. Horner, E. Ozguven","doi":"10.1080/19475683.2022.2026474","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026474","url":null,"abstract":"ABSTRACT Community-wide vaccinations would be the most effective way to end the COVID-19 pandemic, and accessing vaccination sites would be central in this nexus. Given that the number of COVID-19 vaccines was limited to certain groups of people in the early phases of vaccine distribution, age-based prioritization may have overlooked differences in income levels and the races/ethnicities among older populations. In this vein, using two spatial accessibility measures based on spatially disaggregated hexagons, this paper assesses the disparities in spatial accessibility to vaccination sites with consideration of older populations’ (65+) income levels and their races/ethnicities at the state and the county level. To evaluate the disparities and identify counties with the greatest disparities, a non-parametric two-sample Kolmogorov–Smirnov test at the state level and the statistic at the county level are implemented. The findings of this study indicate that older blacks, older Hispanics, and older populations below the poverty level had better access compared to older whites, older non-Hispanics, and older populations above the poverty level, respectively, at the state level, whereas access disparities varied at counties and geographic locales. We thus conclude that policymakers should take into account older populations’ income levels and races/ethnicities for vaccine prioritization and should pay attention to counties with relatively high disparities in spatial access to vaccines.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"4 1","pages":"263 - 277"},"PeriodicalIF":5.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73489758","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 : 2022-01-16DOI: 10.1080/19475683.2022.2026476
Grayson R. Morgan, M. Hodgson, Cuizhen Wang, S. Schill
ABSTRACT Coastal wetlands contribute greatly to our coasts economically and ecologically. The utility of coastal wetland vegetation, along with the multitude of dynamic forces they encounter, suggests the need of regular monitoring for sustainable management. While traditional in situ survey methods and remote sensing from space and manned platforms have provided means to monitor and study the coastal zone thus far, the recent developments of small unmanned aerial systems (sUAS) fill a small void between traditional in situ survey methods and the high spatial resolution of manned aircraft imagery. As an on-demand personal remote sensing device, an sUAS can be deployed over coastal regions at a low cost and with very fine spatial resolution (i.e. 1–10 cm) imagery and corresponding spatial accuracy. Though an sUAS provides many benefits, recent literature documents several shortcomings and limitations to using them for coastal wetland vegetation research, including changing tides, lighting conditions and legal restrictions on flying. This study reviewed all coastal wetland vegetation-related studies that included an sUAS as a mapping tool to document the current state of the field. Current practices, successes, and limitations are described, and future directions for the field are discussed. Coastal managers and researchers alike will be able use this comprehensive review to determine how to best approach future studies of diverse coastal vegetation.
{"title":"Unmanned aerial remote sensing of coastal vegetation: A review","authors":"Grayson R. Morgan, M. Hodgson, Cuizhen Wang, S. Schill","doi":"10.1080/19475683.2022.2026476","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026476","url":null,"abstract":"ABSTRACT Coastal wetlands contribute greatly to our coasts economically and ecologically. The utility of coastal wetland vegetation, along with the multitude of dynamic forces they encounter, suggests the need of regular monitoring for sustainable management. While traditional in situ survey methods and remote sensing from space and manned platforms have provided means to monitor and study the coastal zone thus far, the recent developments of small unmanned aerial systems (sUAS) fill a small void between traditional in situ survey methods and the high spatial resolution of manned aircraft imagery. As an on-demand personal remote sensing device, an sUAS can be deployed over coastal regions at a low cost and with very fine spatial resolution (i.e. 1–10 cm) imagery and corresponding spatial accuracy. Though an sUAS provides many benefits, recent literature documents several shortcomings and limitations to using them for coastal wetland vegetation research, including changing tides, lighting conditions and legal restrictions on flying. This study reviewed all coastal wetland vegetation-related studies that included an sUAS as a mapping tool to document the current state of the field. Current practices, successes, and limitations are described, and future directions for the field are discussed. Coastal managers and researchers alike will be able use this comprehensive review to determine how to best approach future studies of diverse coastal vegetation.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"42 1","pages":"385 - 399"},"PeriodicalIF":5.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87441993","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 : 2022-01-16DOI: 10.1080/19475683.2022.2026468
Ci Song, T. Pei, Xi Wang, Yaxi Liu, Jia Ma, Daojing Zhou
ABSTRACT The novel coronavirus disease of 2019 (COVID-19) first appeared in Wuhan and subsequently spread rapidly in cities and provinces across the country and all over the world. In order to effectively control the spread of the epidemic in different areas, zonal management and endemic prevention and control policies should be implemented according to local epidemic situations. This study proposes a time-series clustering method to discover dynamic characteristics of the COVID-19 epidemic by categorizing the epidemic situations in China’s major cities into groups based on daily reported confirmed cases and analysing the driving factors of the city background conditions for each category. Our results show that according to the dynamic patterns of the COVID-19 epidemic there are eight types of epidemic situations, including extreme outbreak areas, large spread areas, potential resurged areas, middle spread areas, controlled outbreak areas, limited growth areas, delayed outbreak areas, and lag report areas. These dynamic patterns are mainly related to the city background conditions, such as population flow, local resident number, government emergency response capability, and medical resource conditions. Based on our results, different endemic prevention and control measures are recommended for containing the COVID-19 epidemic in cities with different types of epidemic situations.
{"title":"Dynamic characteristics of the COVID-19 epidemic in China’s major cities","authors":"Ci Song, T. Pei, Xi Wang, Yaxi Liu, Jia Ma, Daojing Zhou","doi":"10.1080/19475683.2022.2026468","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026468","url":null,"abstract":"ABSTRACT The novel coronavirus disease of 2019 (COVID-19) first appeared in Wuhan and subsequently spread rapidly in cities and provinces across the country and all over the world. In order to effectively control the spread of the epidemic in different areas, zonal management and endemic prevention and control policies should be implemented according to local epidemic situations. This study proposes a time-series clustering method to discover dynamic characteristics of the COVID-19 epidemic by categorizing the epidemic situations in China’s major cities into groups based on daily reported confirmed cases and analysing the driving factors of the city background conditions for each category. Our results show that according to the dynamic patterns of the COVID-19 epidemic there are eight types of epidemic situations, including extreme outbreak areas, large spread areas, potential resurged areas, middle spread areas, controlled outbreak areas, limited growth areas, delayed outbreak areas, and lag report areas. These dynamic patterns are mainly related to the city background conditions, such as population flow, local resident number, government emergency response capability, and medical resource conditions. Based on our results, different endemic prevention and control measures are recommended for containing the COVID-19 epidemic in cities with different types of epidemic situations.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"14 1","pages":"445 - 456"},"PeriodicalIF":5.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78250326","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 : 2022-01-16DOI: 10.1080/19475683.2022.2027012
Chenyu Zuo, L. Ding, Zhuoni Yang, L. Meng
ABSTRACT Knowledge innovation is a key factor in industrial development and regional economic growth. Understanding regional knowledge innovation and its dynamic changes is one of the fundamental tasks of regional policy-makers and business decision-makers. Although many existing studies have been conducted to support in understanding knowledge innovation patterns, data-driven and intuitive visual analysis of georeferenced knowledge innovation has not been sufficiently studied. In this work, we analysed knowledge innovation by visually exploring big georeferenced scholarly data. More specifically, we first applied network analysis and statistical methods to derive key measures (e.g., the number of publications and academic collaborations) of knowledge innovation with multiple spatial scales. We then designed geovisualizations to explicitly represent the multiscale spatiotemporal patterns and relations. We integrated the analytical methods and geovisualizations into an interactive tool to facilitate stakeholders’ visual learning and analysis of knowledge innovation with a spatial focus. Our work shows that geovisualizations have great potential in supporting complex geoinformation communication in knowledge innovation.
{"title":"Multiscale geovisual analysis of knowledge innovation patterns using big scholarly data","authors":"Chenyu Zuo, L. Ding, Zhuoni Yang, L. Meng","doi":"10.1080/19475683.2022.2027012","DOIUrl":"https://doi.org/10.1080/19475683.2022.2027012","url":null,"abstract":"ABSTRACT Knowledge innovation is a key factor in industrial development and regional economic growth. Understanding regional knowledge innovation and its dynamic changes is one of the fundamental tasks of regional policy-makers and business decision-makers. Although many existing studies have been conducted to support in understanding knowledge innovation patterns, data-driven and intuitive visual analysis of georeferenced knowledge innovation has not been sufficiently studied. In this work, we analysed knowledge innovation by visually exploring big georeferenced scholarly data. More specifically, we first applied network analysis and statistical methods to derive key measures (e.g., the number of publications and academic collaborations) of knowledge innovation with multiple spatial scales. We then designed geovisualizations to explicitly represent the multiscale spatiotemporal patterns and relations. We integrated the analytical methods and geovisualizations into an interactive tool to facilitate stakeholders’ visual learning and analysis of knowledge innovation with a spatial focus. Our work shows that geovisualizations have great potential in supporting complex geoinformation communication in knowledge innovation.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"484 1","pages":"197 - 212"},"PeriodicalIF":5.0,"publicationDate":"2022-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77054817","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 : 2022-01-02DOI: 10.1080/19475683.2022.2030939
Daniel Z. Sui, M. Turner
opics related to the nature of geographic knowledge and the pathway to acquire geographic knowledge have been debated and contested especially during periods of major social and technological change (Harvey 1969; Sayer 1984; Liverman et al. 1998; Sui and Kedron 2021). Tied to these changes has been an oscillation between focuses on phenomenal (declarative) vs. intellectual (primed by cognitive demands) nature of geographic knowledge. Shifting interests in specialities, often triggered by technical innovations in representation and analysis, have constantly changed our views on what is considered as geographic knowledge and challenged our approaches to produce it (Golledge 2002). What remains unchanged is geographers’ continued quest to advance geographic vocabulary, define and examine geographic concepts, and develop spatially explicit theories relating to human, physical environments and their complex interactions. Explorations of interactions between these domains has generated a new interest in advancing general principles and analytical frameworks in geography. Geography is a discipline with a diversity of subfields, including cartography and GIScience as well as human, physical and nature-and-society geography. Despite the enduring debate on whether geography should be an idiographic (aiming to produce phenomenal/declarative knowledge) vs. nomothetic (aiming to develop general principles and theories) discipline, geography has witnessed dramatic specialization within its subfields over the past two decades. This specialization might enable scholars to develop indepth understandings and techniques that better address the issues faced in respective subfields under particular contexts or conditions. These specializations may lead to topical overlap with scholars from other disciplines with whom they still differ by their geographical imagination and approach. Thus, there is a need for geographers to articulate general principles and analytical frameworks that are held in common across the diverse subfields in geography to both better articulate what is common to geography and how it is different from other disciplines and approaches (Sui 2004; Goodchild 2004; Anselin and Li 2020). An important body of work exploring this question, with a focus on spatial relationships, has been produced in the GIScience subfield (http://gistbok.ucgis. org), broadly defined. These treatments have focussed on principles related to spatial variation of phenomena. Spatial autocorrelation (‘Everything is related to everything else, but near things are more related than distant things’) and spatial heterogeneity (‘Geographic variables exhibit uncontrolled variance’) are two important general principles (commonly referred to as first and second laws of geography by some authors), that geographers have offered as important analytical frames for geographic analyses. Recently, a possible third principle, geographic similarity (‘The more similar geographic configurations
与地理知识的性质和获取地理知识的途径有关的主题一直受到辩论和争议，特别是在重大的社会和技术变革时期(Harvey 1969;说话的人1984;Liverman et al. 1998;Sui and Kedron 2021)。与这些变化相关的是地理知识的现象性(陈述性)与知识性(由认知需求启动)之间的摇摆。对专业的兴趣不断变化，通常是由表现和分析方面的技术创新引发的，不断改变了我们对什么是地理知识的看法，并挑战了我们产生地理知识的方法(Golledge 2002)。保持不变的是地理学家继续追求推进地理词汇，定义和研究地理概念，并发展与人类，物理环境及其复杂相互作用有关的空间明确理论。这些领域之间的相互作用的探索产生了新的兴趣，在推进一般原则和分析框架的地理学。地理学是一门具有多种子领域的学科，包括地图学和地理信息科学以及人文地理学、自然地理学和自然与社会地理学。尽管地理学应该是一门具体学科(旨在产生现象性/陈述性知识)还是一门学科(旨在发展一般原则和理论)一直存在争议，但在过去的二十年里，地理学在其子领域内见证了戏剧性的专业化。这种专业化可能使学者能够深入理解和发展技术，更好地解决在特定背景或条件下各自子领域面临的问题。这些专业可能会导致与其他学科的学者在主题上重叠，他们在地理想象力和方法上仍然不同。因此，地理学家有必要阐明在地理学的不同子领域中共同持有的一般原则和分析框架，以更好地阐明地理学的共同点以及它与其他学科和方法的不同之处(Sui 2004;Goodchild 2004;Anselin and Li 2020)。在GIScience子领域(http://gistbok.ucgis)已经产生了一个以空间关系为重点的探索这个问题的重要工作体。Org)，广义的定义。这些处理侧重于与现象的空间变异有关的原则。空间自相关(“所有事物都与其他事物相关，但近的事物比远的事物更相关”)和空间异质性(“地理变量表现出不受控制的方差”)是两个重要的一般原则(一些作者通常将其称为地理第一定律和第二定律)，地理学家将其作为地理分析的重要分析框架。最近，可能的第三个原则，地理相似性(“两点的地理结构越相似，这两点的目标变量的值(过程)就越相似”)，被提出作为另一个一般分析框架。它与前两个相结合，开辟了新的途径，参与正在进行的关于规模、地点、关系、背景和地理和科学各个子领域整合等问题的辩论。在考虑这些原则/规律时，提出了一些问题:1)上述一般原则(空间自相关、空间异质性、地理相似性)是否适用于地理学家和地理科学工作者的分析框架?2)这些原则如何与其他子领域的新兴概念和框架相关联(Dunn 2021)?3)如果有的话，从最近的文献中出现了什么新的原则(规律)和分析框架来应对地理和地理科学的新挑战?
{"title":"General theories and principles in geography and GIScience: Moving beyond the idiographic and nomothetic dichotomy","authors":"Daniel Z. Sui, M. Turner","doi":"10.1080/19475683.2022.2030939","DOIUrl":"https://doi.org/10.1080/19475683.2022.2030939","url":null,"abstract":"opics related to the nature of geographic knowledge and the pathway to acquire geographic knowledge have been debated and contested especially during periods of major social and technological change (Harvey 1969; Sayer 1984; Liverman et al. 1998; Sui and Kedron 2021). Tied to these changes has been an oscillation between focuses on phenomenal (declarative) vs. intellectual (primed by cognitive demands) nature of geographic knowledge. Shifting interests in specialities, often triggered by technical innovations in representation and analysis, have constantly changed our views on what is considered as geographic knowledge and challenged our approaches to produce it (Golledge 2002). What remains unchanged is geographers’ continued quest to advance geographic vocabulary, define and examine geographic concepts, and develop spatially explicit theories relating to human, physical environments and their complex interactions. Explorations of interactions between these domains has generated a new interest in advancing general principles and analytical frameworks in geography. Geography is a discipline with a diversity of subfields, including cartography and GIScience as well as human, physical and nature-and-society geography. Despite the enduring debate on whether geography should be an idiographic (aiming to produce phenomenal/declarative knowledge) vs. nomothetic (aiming to develop general principles and theories) discipline, geography has witnessed dramatic specialization within its subfields over the past two decades. This specialization might enable scholars to develop indepth understandings and techniques that better address the issues faced in respective subfields under particular contexts or conditions. These specializations may lead to topical overlap with scholars from other disciplines with whom they still differ by their geographical imagination and approach. Thus, there is a need for geographers to articulate general principles and analytical frameworks that are held in common across the diverse subfields in geography to both better articulate what is common to geography and how it is different from other disciplines and approaches (Sui 2004; Goodchild 2004; Anselin and Li 2020). An important body of work exploring this question, with a focus on spatial relationships, has been produced in the GIScience subfield (http://gistbok.ucgis. org), broadly defined. These treatments have focussed on principles related to spatial variation of phenomena. Spatial autocorrelation (‘Everything is related to everything else, but near things are more related than distant things’) and spatial heterogeneity (‘Geographic variables exhibit uncontrolled variance’) are two important general principles (commonly referred to as first and second laws of geography by some authors), that geographers have offered as important analytical frames for geographic analyses. Recently, a possible third principle, geographic similarity (‘The more similar geographic configurations ","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"9 1","pages":"1 - 4"},"PeriodicalIF":5.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87667342","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 : 2022-01-02DOI: 10.1080/19475683.2022.2026473
Bin Li, D. Griffith
ABSTRACT Geoinformatic Tupu, or Geoinformatic graph spectrum, is a theoretical as well as a technical framework for generalizing geographic knowledge and solving real world problems. Geoinformatic Tupu is a promising platform for capitalizing on the technical advances of Geographic Information Systems, and to integrate the Chinese traditional way of thinking with modern information technology. It has been one of the major research topics in the Chinese GIScience community in recent decades, with an evolving epistemological development. A core objective of Geoinformatic Tupu is to recover and represent geographic principles with the Tupu approach, which is adopted in this paper to formulate the First Law of Geography (FLG) [i.e. the law of spatial autocorrelation] as the Moran Spectrum – a combination of sequential diagrams, graphs, and numeric components. Using the Moran Spectrum as a conduit, we present the theory of Moran Eigenvector Spatial Filtering (MESF), a distinct branch of spatial statistics that has demonstrable advantages in statistical modelling and machine learning, but has yet to be widely disseminated due to its conceptual and computational complexity. This paper demonstrates the effectiveness of the Tupu approach in enriching the representation of the FLG as well as deepening its applications. It also suggests inclusion of the Moran Spectrum as a core component in Geoinformatic Tupu.
{"title":"The Moran Spectrum as a Geoinformatic Tupu: implications for the First Law of Geography","authors":"Bin Li, D. Griffith","doi":"10.1080/19475683.2022.2026473","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026473","url":null,"abstract":"ABSTRACT Geoinformatic Tupu, or Geoinformatic graph spectrum, is a theoretical as well as a technical framework for generalizing geographic knowledge and solving real world problems. Geoinformatic Tupu is a promising platform for capitalizing on the technical advances of Geographic Information Systems, and to integrate the Chinese traditional way of thinking with modern information technology. It has been one of the major research topics in the Chinese GIScience community in recent decades, with an evolving epistemological development. A core objective of Geoinformatic Tupu is to recover and represent geographic principles with the Tupu approach, which is adopted in this paper to formulate the First Law of Geography (FLG) [i.e. the law of spatial autocorrelation] as the Moran Spectrum – a combination of sequential diagrams, graphs, and numeric components. Using the Moran Spectrum as a conduit, we present the theory of Moran Eigenvector Spatial Filtering (MESF), a distinct branch of spatial statistics that has demonstrable advantages in statistical modelling and machine learning, but has yet to be widely disseminated due to its conceptual and computational complexity. This paper demonstrates the effectiveness of the Tupu approach in enriching the representation of the FLG as well as deepening its applications. It also suggests inclusion of the Moran Spectrum as a core component in Geoinformatic Tupu.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"2 1","pages":"69 - 83"},"PeriodicalIF":5.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89307756","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 : 2022-01-02DOI: 10.1080/19475683.2022.2030943
M. Goodchild
Geography and GIScience (geographic information science) are both concerned as disciplines with the infinite complexity of the surface and near-surface of the Earth, or what we might call the geographic domain. Many other disciplines also concern themselves with this domain, including most if not all of the social and environmental sciences, but none do so with the generality of geography and GIScience. Geography has a long tradition of concern with integration, with exploring the links that exist between disciplines and with problems whose solution requires knowledge that extends across many disciplines. It is not surprising, therefore, that an invitation to address the general principles and analytical frameworks in geography and GIScience has generated such a diversity of perspectives. There are clearly many questions one might ask about the geographic domain, and many routes to building representations that might be used to address those questions, especially when those representations must capture many distinct phenomena in the same framework. Geographers have long used maps as a framework with which to create, store and share representations of the geographic domain. But maps have obvious limitations: they are flat while the geographic domain is curved; they use two spatial dimensions to represent the three spatial dimensions of the domain; they must necessarily focus on static features; unlike numerical data, they are not readily submitted to quantitative analysis; and the scale of a map imposes a constraint on the representation’s level of detail. Today, the move to digital representations has in principle removed many of these limitations. Geographic information systems (GIS) and spatial databases now capture, represent and analyse the information that was previously shown in maps; they include the third spatial dimension; and it is now possible to represent and investigate time-dependent phenomena. Thus, tupu, the concept advanced by Chen Shupeng and the subject of Li’s paper (Li, this volume), is in many ways the guiding principle of today’s spatiotemporal databases. Although there have been very important advances in the capturing of greater detail, spatial and temporal resolution must always remain limited to some degree because of the limitations of our observing systems. Moreover, practice is often slow to adjust to new opportunities, and many of the decisions made in the early days of the digital transition, at a time when computational resources were extremely limited, still have their legacy effects today (Goodchild 2018). Clearly, any general principles that might apply to the geographic domain would be extremely valuable as a basis for digital representation and analytic frameworks, and several are identified in these papers. Many make reference to the principle of spatial dependence, nicely expressed by Tobler (1970) in what he suggested might qualify as a First Law of Geography: nearby things are more similar than distant th
{"title":"Commentary: general principles and analytical frameworks in geography and GIScience","authors":"M. Goodchild","doi":"10.1080/19475683.2022.2030943","DOIUrl":"https://doi.org/10.1080/19475683.2022.2030943","url":null,"abstract":"Geography and GIScience (geographic information science) are both concerned as disciplines with the infinite complexity of the surface and near-surface of the Earth, or what we might call the geographic domain. Many other disciplines also concern themselves with this domain, including most if not all of the social and environmental sciences, but none do so with the generality of geography and GIScience. Geography has a long tradition of concern with integration, with exploring the links that exist between disciplines and with problems whose solution requires knowledge that extends across many disciplines. It is not surprising, therefore, that an invitation to address the general principles and analytical frameworks in geography and GIScience has generated such a diversity of perspectives. There are clearly many questions one might ask about the geographic domain, and many routes to building representations that might be used to address those questions, especially when those representations must capture many distinct phenomena in the same framework. Geographers have long used maps as a framework with which to create, store and share representations of the geographic domain. But maps have obvious limitations: they are flat while the geographic domain is curved; they use two spatial dimensions to represent the three spatial dimensions of the domain; they must necessarily focus on static features; unlike numerical data, they are not readily submitted to quantitative analysis; and the scale of a map imposes a constraint on the representation’s level of detail. Today, the move to digital representations has in principle removed many of these limitations. Geographic information systems (GIS) and spatial databases now capture, represent and analyse the information that was previously shown in maps; they include the third spatial dimension; and it is now possible to represent and investigate time-dependent phenomena. Thus, tupu, the concept advanced by Chen Shupeng and the subject of Li’s paper (Li, this volume), is in many ways the guiding principle of today’s spatiotemporal databases. Although there have been very important advances in the capturing of greater detail, spatial and temporal resolution must always remain limited to some degree because of the limitations of our observing systems. Moreover, practice is often slow to adjust to new opportunities, and many of the decisions made in the early days of the digital transition, at a time when computational resources were extremely limited, still have their legacy effects today (Goodchild 2018). Clearly, any general principles that might apply to the geographic domain would be extremely valuable as a basis for digital representation and analytic frameworks, and several are identified in these papers. Many make reference to the principle of spatial dependence, nicely expressed by Tobler (1970) in what he suggested might qualify as a First Law of Geography: nearby things are more similar than distant th","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"90 1","pages":"85 - 87"},"PeriodicalIF":5.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72869856","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 : 2022-01-02DOI: 10.1080/19475683.2022.2026467
A-Xing Zhu, M. Turner
ABSTRACT Three overarching principles governing patterns of geographic phenomena have been proposed that have been referred to by some as ‘laws of geography’. The first and the second principles address the spatial proximity and spatial heterogeneity of geographic phenomena. These principles, while powerful, fail to resonate with much geographical inquiry. The more recently proposed third principle concerns geographic similarity. The differences of it from the first two can be perceived in three basic aspects: principle expressed, form of expression and role of geographic examples (samples). The third principle emphasizes the geographic context of geographic variables in the form of geographic configuration, compared to a single spatial dimension that are emphasized in the first two principles. The third principle focuses on the comparative nature in the geographic configuration in terms of similarity, that is, in the form of ‘similar to’, as opposed to the relationships ‘between’ that are key to the first and second principles. The third principle emphasizes the individual representation of geographic examples, as opposed to the global representation of geographic examples. These differences not only distinguish the third principle as an important addition to the other two, but also provide a potentially transformative way to address the rigid requirements on samples in geographic analysis, particularly during this age when the collection and provision of geographic data are crowd-sourced and VGI-based. These differences also point to the potential of the third principle opening up a space of inquiry that would resonate more successfully with place-based approaches in human geography.
{"title":"How is the Third Law of Geography different?","authors":"A-Xing Zhu, M. Turner","doi":"10.1080/19475683.2022.2026467","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026467","url":null,"abstract":"ABSTRACT Three overarching principles governing patterns of geographic phenomena have been proposed that have been referred to by some as ‘laws of geography’. The first and the second principles address the spatial proximity and spatial heterogeneity of geographic phenomena. These principles, while powerful, fail to resonate with much geographical inquiry. The more recently proposed third principle concerns geographic similarity. The differences of it from the first two can be perceived in three basic aspects: principle expressed, form of expression and role of geographic examples (samples). The third principle emphasizes the geographic context of geographic variables in the form of geographic configuration, compared to a single spatial dimension that are emphasized in the first two principles. The third principle focuses on the comparative nature in the geographic configuration in terms of similarity, that is, in the form of ‘similar to’, as opposed to the relationships ‘between’ that are key to the first and second principles. The third principle emphasizes the individual representation of geographic examples, as opposed to the global representation of geographic examples. These differences not only distinguish the third principle as an important addition to the other two, but also provide a potentially transformative way to address the rigid requirements on samples in geographic analysis, particularly during this age when the collection and provision of geographic data are crowd-sourced and VGI-based. These differences also point to the potential of the third principle opening up a space of inquiry that would resonate more successfully with place-based approaches in human geography.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"32 1","pages":"57 - 67"},"PeriodicalIF":5.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87927064","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 : 2022-01-02DOI: 10.1080/19475683.2022.2027011
P. Kedron, J. Holler
ABSTRACT Replication is a means of assessing the credibility and generalizability of scientific results, whereby subsequent studies independently corroborate the findings of initial research. In the study of geographic phenomena, a distinct form of replicability is particularly important – whether a result obtained in one geographic context applies in another geographic context. However, the laws of geography suggest that it may be challenging to use replication to assess the credibility of findings across space and to identify new laws. Many geographic phenomena are spatially heterogeneous, which implies they exhibit uncontrolled variance across the surface of the earth and lack a characteristic mean. When a phenomenon is spatially heterogeneous, it may be difficult or impossible to establish baselines or rules for study-to-study comparisons. At the same time, geographic observations are typically spatially dependent, which makes it difficult to isolate the effects of interest for cross-study comparison. In this paper, we discuss how laws describing the spatial variation of phenomena may influence the use of replication in geographic research. Developing a set of shared principles for replication assessment based on fundamental laws of geography is a prerequisite for adapting replication standards to meet the needs of disciplinary subfields while maintaining a shared analytical foundation for convergent spatial research.
{"title":"Replication and the search for the laws in the geographic sciences","authors":"P. Kedron, J. Holler","doi":"10.1080/19475683.2022.2027011","DOIUrl":"https://doi.org/10.1080/19475683.2022.2027011","url":null,"abstract":"ABSTRACT Replication is a means of assessing the credibility and generalizability of scientific results, whereby subsequent studies independently corroborate the findings of initial research. In the study of geographic phenomena, a distinct form of replicability is particularly important – whether a result obtained in one geographic context applies in another geographic context. However, the laws of geography suggest that it may be challenging to use replication to assess the credibility of findings across space and to identify new laws. Many geographic phenomena are spatially heterogeneous, which implies they exhibit uncontrolled variance across the surface of the earth and lack a characteristic mean. When a phenomenon is spatially heterogeneous, it may be difficult or impossible to establish baselines or rules for study-to-study comparisons. At the same time, geographic observations are typically spatially dependent, which makes it difficult to isolate the effects of interest for cross-study comparison. In this paper, we discuss how laws describing the spatial variation of phenomena may influence the use of replication in geographic research. Developing a set of shared principles for replication assessment based on fundamental laws of geography is a prerequisite for adapting replication standards to meet the needs of disciplinary subfields while maintaining a shared analytical foundation for convergent spatial research.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"94 1","pages":"45 - 56"},"PeriodicalIF":5.0,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85652184","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：