Pub Date : 2022-06-07DOI: 10.1080/19475683.2022.2071337
M. Modiri, Y. Gholami, S. Hosseini
ABSTRACT The spatial models dealing with urban growth dynamics have been widely studied, while rare works have considered under-developed countries. Several problems have been detected in creating, calibrating and applying urban growth models and changing land use. The present work aims the modelling land-use changes through the CA-GA model in which a frame is provided for analysis and producing a map of growth patterns in urban areas in different spatial scales to study and analyse the increasing urban growth in Tehran. To consider the land-use changes in Tehran, ETM+TM images of 1985, 1992, 2000, and 2020 were selected to be analysed by the CA-GA algorithm to model the growth of the urban areas. The total kappa of results in Tehran is about 0.93, indicating the required precision and confidence of applied combinative genetic-Cellular automata modelling methods to model urban development.
{"title":"Urban growth dynamics modeling through urban DNA in Tehran metropolitan region","authors":"M. Modiri, Y. Gholami, S. Hosseini","doi":"10.1080/19475683.2022.2071337","DOIUrl":"https://doi.org/10.1080/19475683.2022.2071337","url":null,"abstract":"ABSTRACT The spatial models dealing with urban growth dynamics have been widely studied, while rare works have considered under-developed countries. Several problems have been detected in creating, calibrating and applying urban growth models and changing land use. The present work aims the modelling land-use changes through the CA-GA model in which a frame is provided for analysis and producing a map of growth patterns in urban areas in different spatial scales to study and analyse the increasing urban growth in Tehran. To consider the land-use changes in Tehran, ETM+TM images of 1985, 1992, 2000, and 2020 were selected to be analysed by the CA-GA algorithm to model the growth of the urban areas. The total kappa of results in Tehran is about 0.93, indicating the required precision and confidence of applied combinative genetic-Cellular automata modelling methods to model urban development.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"41 1","pages":"55 - 74"},"PeriodicalIF":5.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82668949","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 : 2022-04-03DOI: 10.1080/19475683.2022.2070969
Yijing Li, Qunshan Zhao, Chen Zhong
ABSTRACT With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research.
{"title":"GIS and urban data science","authors":"Yijing Li, Qunshan Zhao, Chen Zhong","doi":"10.1080/19475683.2022.2070969","DOIUrl":"https://doi.org/10.1080/19475683.2022.2070969","url":null,"abstract":"ABSTRACT With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"22 1","pages":"89 - 92"},"PeriodicalIF":5.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72693328","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 : 2022-03-20DOI: 10.1080/19475683.2022.2052748
Daniel Salafranca Barreda, Diego J. Maldonado-Guzmán, Patricia Saldaña-Taboada
ABSTRACT The edge effect is a problem that can alter the results of some analyses, such as counting crime within a given geographic area. This article introduces a tool developed for ArcGIS toolbox, (ArcGIS Geographic Information System) to correct the border issues when using an aggregated crime data to artificially bounded space analytical units. It uses a method which considers those points located near the edge of the analysis unit, and avoids increasing the number of criminal points by assigning a value according to the distance of the edge. For this purpose, two functions based on decay with distance can be chosen: normal and linear. In order to show the performance of the tool, a sample of theft data occurred in 2016 in each census tract of Barcelona (Spain) district was used. These results show remarkable differences in the number of thefts in each census tract, before and after applying the edge correction. Some of the census tracts even went from experiencing no theft at all to having 5.5 or 4.5 incidents after correcting the edge effect. Finally, to demonstrate the benefits of the proposed tool, other strategies traditionally used as a solution for the edge effect were used. Then, the results are compared with those previously obtained.
边缘效应是一个可以改变某些分析结果的问题，例如在给定地理区域内计算犯罪。本文介绍了一种针对ArcGIS工具箱开发的工具，即ArcGIS地理信息系统(ArcGIS Geographic Information System)，在使用汇总的犯罪数据对人为有界空间进行分析单元时纠正边界问题。它采用了一种考虑靠近分析单元边缘的点的方法，并通过根据边缘的距离分配值来避免增加犯罪点的数量。为此，可以选择两种基于距离衰减的函数:法向函数和线性函数。为了展示该工具的性能，使用了2016年在巴塞罗那(西班牙)地区每个人口普查区发生的盗窃数据样本。这些结果表明，在应用边缘校正之前和之后，每个人口普查区的盗窃数量存在显著差异。在修正了边缘效应后，一些人口普查区甚至从完全没有盗窃事件变成了5.5或4.5起。最后，为了证明所提出的工具的好处，使用了传统上用作边缘效应解决方案的其他策略。然后，将所得结果与之前的结果进行了比较。
{"title":"Crime beyond the edge: development of a tool to correct the edge effect on crime count","authors":"Daniel Salafranca Barreda, Diego J. Maldonado-Guzmán, Patricia Saldaña-Taboada","doi":"10.1080/19475683.2022.2052748","DOIUrl":"https://doi.org/10.1080/19475683.2022.2052748","url":null,"abstract":"ABSTRACT The edge effect is a problem that can alter the results of some analyses, such as counting crime within a given geographic area. This article introduces a tool developed for ArcGIS toolbox, (ArcGIS Geographic Information System) to correct the border issues when using an aggregated crime data to artificially bounded space analytical units. It uses a method which considers those points located near the edge of the analysis unit, and avoids increasing the number of criminal points by assigning a value according to the distance of the edge. For this purpose, two functions based on decay with distance can be chosen: normal and linear. In order to show the performance of the tool, a sample of theft data occurred in 2016 in each census tract of Barcelona (Spain) district was used. These results show remarkable differences in the number of thefts in each census tract, before and after applying the edge correction. Some of the census tracts even went from experiencing no theft at all to having 5.5 or 4.5 incidents after correcting the edge effect. Finally, to demonstrate the benefits of the proposed tool, other strategies traditionally used as a solution for the edge effect were used. Then, the results are compared with those previously obtained.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"280 1 1","pages":"279 - 292"},"PeriodicalIF":5.0,"publicationDate":"2022-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86172966","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 : 2022-03-03DOI: 10.1080/19475683.2022.2037019
Ibrahim M. Badwi, Hisham M. Ellaithy, Hidi E. Youssef
ABSTRACT Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city’s virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database.
{"title":"3D-GIS Parametric Modelling for Virtual Urban Simulation Using CityEngine","authors":"Ibrahim M. Badwi, Hisham M. Ellaithy, Hidi E. Youssef","doi":"10.1080/19475683.2022.2037019","DOIUrl":"https://doi.org/10.1080/19475683.2022.2037019","url":null,"abstract":"ABSTRACT Modelling and visualization of three-dimensional (3D) models for cities is a great challenge for computer software and graphics. Recently, 3D city modelling has grown due to advances in applications accompanying the information technology revolution. 3D Geographic Information Systems (3D-GIS) have evolved enormously due to the availability of large-scale 3D modelling techniques. These technologies have become very important in representing large cities and conducting various analyses in the city’s virtual environment to support urban decision-making. CityEngine is one of the most recent 3D-GIS modelling applications. CityEngine can be described as parametric modelling using Procedural Modelling (PM) to create 3D urban elements through macros and routines. This paper highlights the importance of 3D Procedural Modelling (PM) of cities in the GIS environment using ESRI CityEngine and presents a parametric concept for designing urban spaces. This issue has been addressed in three respects. First, discuss the concept and strength of parametric design. Second, the concept of procedural modelling and its power to generate complex 3D models using a set of rules is discussed. Finally, CityEngine was evaluated through a real-world case study of a neighbourhood in the new city of Beni-Suef, Egypt. The results confirm the effectiveness of CityEngine as a 3D-GIS modelling software that generates dynamic 3D models from 2D spatial data. While the results are promising, it is important to investigate more complex cases. The CityEngine modelling approach enables comprehensive urban analyses such as sequence vision, façade studies, urban fabric and character, and statistical operations based on attribute database.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"32 1","pages":"325 - 341"},"PeriodicalIF":5.0,"publicationDate":"2022-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90716895","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 : 2022-03-01DOI: 10.1080/19475683.2022.2044903
Weiyu Wang, J. Blossom, J. Kim, P. deSouza, Weixing Zhang, Rockli Kim, R. Sarwal, S. Subramanian
ABSTRACT In India, Parliamentary Constituencies (PCs) could serve as a regional unit of COVID-19 monitoring that facilitates evidence-based policy decisions. In this study, we presented the first estimates of COVID-19 cumulative cases and deaths per 100,000 population and the case fatality rate (CFR) between 7 January 2020 and 31 January 2021 across PCs and districts of India. We adopted a novel geographic information science-based methodology called crosswalk to estimate COVID-19 outcomes at the PC-level from district-level information. We found a substantial variation of COVID-19 burden within each state and across the country. Access to PC-level and district-level COVID-19 information can enhance both central and regional governmental accountability of safe reopening policies.
{"title":"COVID-19 metrics across parliamentary constituencies and districts in India","authors":"Weiyu Wang, J. Blossom, J. Kim, P. deSouza, Weixing Zhang, Rockli Kim, R. Sarwal, S. Subramanian","doi":"10.1080/19475683.2022.2044903","DOIUrl":"https://doi.org/10.1080/19475683.2022.2044903","url":null,"abstract":"ABSTRACT In India, Parliamentary Constituencies (PCs) could serve as a regional unit of COVID-19 monitoring that facilitates evidence-based policy decisions. In this study, we presented the first estimates of COVID-19 cumulative cases and deaths per 100,000 population and the case fatality rate (CFR) between 7 January 2020 and 31 January 2021 across PCs and districts of India. We adopted a novel geographic information science-based methodology called crosswalk to estimate COVID-19 outcomes at the PC-level from district-level information. We found a substantial variation of COVID-19 burden within each state and across the country. Access to PC-level and district-level COVID-19 information can enhance both central and regional governmental accountability of safe reopening policies.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"75 6 1","pages":"435 - 443"},"PeriodicalIF":5.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91031033","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 : 2022-02-28DOI: 10.1080/19475683.2022.2040587
N. Gupta, S. Pal, J. Das
ABSTRACT The main sought of this study is to assess the landslide susceptibility map (LSM) of the East Sikkim Himalaya based on the comparative model analysis using frequency ratio (FR), logistic regression (LR), random forest (RF) and integration of analytical hierarchy process (AHP) with FR (AHP-FR). The models were trained by 166 landslides (70% training) and 12 landslide causative factors whilst tested with the help of 71 landslides (30% testing). Their spatial correlation between the landslides and the causative factors was analysed by using a multicollinearity test. The generated LSM was classified into five classes, i.e. very low, low, moderate, high and very high. In East Sikkim, very high classes of the AHP-FR, LR and FR models cover the area of 11.97%, 11.99% and 7.13%, respectively. The accuracy of prepared LSM was evaluated by using the success rate curve (SRC), prediction rate curve (PRC) and seed calculation area index (SCAI). The area under the curve (AUC) of the success rate curve is 0.88 for the RF model, 0.85 for AHP-FR, 0.78 for LR and 0.79 for FR, whilst the prediction rate curve is 0.86% for the RF model, 0.81 for AHP-FR, 0.79 for LR and 0.78 for FR. The SCAI values of very high susceptibility classes are 0.14, 0.17, 0.18 and 0.19 for the RF, AHP-FR, LR and FR models, respectively. The RF and integrated AHP-FR methods indicate better results as compared to other statistical models.
{"title":"GIS-based evolution and comparisons of landslide susceptibility mapping of the East Sikkim Himalaya","authors":"N. Gupta, S. Pal, J. Das","doi":"10.1080/19475683.2022.2040587","DOIUrl":"https://doi.org/10.1080/19475683.2022.2040587","url":null,"abstract":"ABSTRACT The main sought of this study is to assess the landslide susceptibility map (LSM) of the East Sikkim Himalaya based on the comparative model analysis using frequency ratio (FR), logistic regression (LR), random forest (RF) and integration of analytical hierarchy process (AHP) with FR (AHP-FR). The models were trained by 166 landslides (70% training) and 12 landslide causative factors whilst tested with the help of 71 landslides (30% testing). Their spatial correlation between the landslides and the causative factors was analysed by using a multicollinearity test. The generated LSM was classified into five classes, i.e. very low, low, moderate, high and very high. In East Sikkim, very high classes of the AHP-FR, LR and FR models cover the area of 11.97%, 11.99% and 7.13%, respectively. The accuracy of prepared LSM was evaluated by using the success rate curve (SRC), prediction rate curve (PRC) and seed calculation area index (SCAI). The area under the curve (AUC) of the success rate curve is 0.88 for the RF model, 0.85 for AHP-FR, 0.78 for LR and 0.79 for FR, whilst the prediction rate curve is 0.86% for the RF model, 0.81 for AHP-FR, 0.79 for LR and 0.78 for FR. The SCAI values of very high susceptibility classes are 0.14, 0.17, 0.18 and 0.19 for the RF, AHP-FR, LR and FR models, respectively. The RF and integrated AHP-FR methods indicate better results as compared to other statistical models.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"145 1","pages":"359 - 384"},"PeriodicalIF":5.0,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72806555","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 : 2022-02-22DOI: 10.1080/19475683.2022.2043937
A. Stein, C. Detotto, Mariana Belgiu
ABSTRACT The spatial distribution of nuraghes throughout the Island of Sardinia still raises many questions. In this paper, we apply spatial statistical methods to investigate their relations with topographical features and with related objects nearby. We use the non-stationary G- and J-functions. To model interactions with topographic variables we use the non-stationary Poisson model. We find that the elevation of the nuraghes show a uniform distribution between 0 and 400 m, and with a peak in distances to holy wells of approximately 5 km. As expected, we found a clustered pattern, with clustering occurring in the mid-west, the centre and the south west of the Island. We further observed a very strong interaction with domus de janas, and a strong spatial interaction for distances in the range between 0 and 1000 m with the pre-Nuragic dolmens and menhirs, and the collective funerary structures, the so-called Nuragic giants’ tombs. We conclude that the study is useful to quantify spatial patterns of pre-historic sites, in particular if these occur in a great abundancy and provides new insight into the spatial relations of the different pre-historic objects and buildings.
撒丁岛努拉格的空间分布仍然存在许多问题。本文应用空间统计方法研究了它们与地形特征和附近相关物体的关系。我们使用非平稳G和j函数。为了模拟与地形变量的相互作用，我们使用非平稳泊松模型。我们发现nuraghes的海拔在0到400米之间呈均匀分布，并且在距离圣井约5公里的距离上有一个峰值。正如预期的那样，我们发现了一个集群模式，集群发生在岛的中西部、中部和西南部。我们进一步观察到与domus de janas的强烈相互作用，以及与前努拉吉时代的石碑和石碑以及集体丧葬结构(即所谓的努拉吉时代巨人的坟墓)在0到1000米范围内的强烈空间相互作用。我们的结论是，该研究有助于量化史前遗址的空间格局，特别是如果这些遗址大量出现，并为不同史前物体和建筑的空间关系提供新的见解。
{"title":"A spatial statistical study of the distribution of Sardinian nuraghes","authors":"A. Stein, C. Detotto, Mariana Belgiu","doi":"10.1080/19475683.2022.2043937","DOIUrl":"https://doi.org/10.1080/19475683.2022.2043937","url":null,"abstract":"ABSTRACT The spatial distribution of nuraghes throughout the Island of Sardinia still raises many questions. In this paper, we apply spatial statistical methods to investigate their relations with topographical features and with related objects nearby. We use the non-stationary G- and J-functions. To model interactions with topographic variables we use the non-stationary Poisson model. We find that the elevation of the nuraghes show a uniform distribution between 0 and 400 m, and with a peak in distances to holy wells of approximately 5 km. As expected, we found a clustered pattern, with clustering occurring in the mid-west, the centre and the south west of the Island. We further observed a very strong interaction with domus de janas, and a strong spatial interaction for distances in the range between 0 and 1000 m with the pre-Nuragic dolmens and menhirs, and the collective funerary structures, the so-called Nuragic giants’ tombs. We conclude that the study is useful to quantify spatial patterns of pre-historic sites, in particular if these occur in a great abundancy and provides new insight into the spatial relations of the different pre-historic objects and buildings.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"1 1","pages":"245 - 261"},"PeriodicalIF":5.0,"publicationDate":"2022-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90393220","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 : 2022-02-14DOI: 10.1080/19475683.2022.2026475
A. H. N. Mfondoum, Sofia Hakdaoui, Roseline Batcha
ABSTRACT This paper explores a spectral vector-based methodology on Landsat 8 bands of the visible wavelengths, that is deep-blue (1) to shortwave infrared (7), to improve the urban land features classification. Using two different ratio models, based on two and three bands’ combinations in the cloud environment of Google Earth Engine, the Uncertainty reducing Spectral Vector (USVr), the Onward Continuous Spectral Vector (OSVc) and the Onward Discontinuous Spectral Vector (OSVd) are proposed as new entries for the land use land cover (LULC) classification. Two different sizes of arrays are built, i.e. 42 vectors and 15 vectors corresponding to the same number of derivative bands and new pixels′ values. A decision tree is built in J.48 and applied to select the most suitable derivative bands for the analysis. Hereafter, the selected ones are stacked and submitted to five machine learning classifiers using a supervised process, namely, Classification and Regression Trees (CART), Random Forest (RF) Gradient Boosting (GBR), Support Vector Machine (SVM) and Minimum Distance (MD). This method was tested in the two cities of Bamenda and Foumban in west-Cameroon highlands, due to their good representativeness of tropical hilly urban areas’ spatial heterogeneity. The results are satisfying for 4/5 classifiers, up to 87% Overall Accuracy, OA, for 0.82 kappa coefficient, KC, in Bamenda, while combining SVM/OSVd. Whereas, in Foumban, the classifiers perform up to 85%OA and 0.78 KC for the combination SVM/USVr. Only the MD classifier has always performed below 80%OA. The process has been found better than performing classifiers directly on the multispectral (MS) image, by providing more possibilities of hidden spectral indices not yet explored, as far as we know, to detect and discriminate between LULC features, plus an accurate extraction of human settlements.
{"title":"Landsat 8Bands’ 1 to 7 spectral vectors plus machine learning to improve land use/cover classification using Google Earth Engine","authors":"A. H. N. Mfondoum, Sofia Hakdaoui, Roseline Batcha","doi":"10.1080/19475683.2022.2026475","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026475","url":null,"abstract":"ABSTRACT This paper explores a spectral vector-based methodology on Landsat 8 bands of the visible wavelengths, that is deep-blue (1) to shortwave infrared (7), to improve the urban land features classification. Using two different ratio models, based on two and three bands’ combinations in the cloud environment of Google Earth Engine, the Uncertainty reducing Spectral Vector (USVr), the Onward Continuous Spectral Vector (OSVc) and the Onward Discontinuous Spectral Vector (OSVd) are proposed as new entries for the land use land cover (LULC) classification. Two different sizes of arrays are built, i.e. 42 vectors and 15 vectors corresponding to the same number of derivative bands and new pixels′ values. A decision tree is built in J.48 and applied to select the most suitable derivative bands for the analysis. Hereafter, the selected ones are stacked and submitted to five machine learning classifiers using a supervised process, namely, Classification and Regression Trees (CART), Random Forest (RF) Gradient Boosting (GBR), Support Vector Machine (SVM) and Minimum Distance (MD). This method was tested in the two cities of Bamenda and Foumban in west-Cameroon highlands, due to their good representativeness of tropical hilly urban areas’ spatial heterogeneity. The results are satisfying for 4/5 classifiers, up to 87% Overall Accuracy, OA, for 0.82 kappa coefficient, KC, in Bamenda, while combining SVM/OSVd. Whereas, in Foumban, the classifiers perform up to 85%OA and 0.78 KC for the combination SVM/USVr. Only the MD classifier has always performed below 80%OA. The process has been found better than performing classifiers directly on the multispectral (MS) image, by providing more possibilities of hidden spectral indices not yet explored, as far as we know, to detect and discriminate between LULC features, plus an accurate extraction of human settlements.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"352 1","pages":"401 - 424"},"PeriodicalIF":5.0,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76593836","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 : 2022-01-27DOI: 10.1080/19475683.2022.2031293
Tianyu Li
ABSTRACT Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry’s interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians.
{"title":"Exploring spatial variations of US rock music concerts in relation to population demographics and leisure and hospitality industry","authors":"Tianyu Li","doi":"10.1080/19475683.2022.2031293","DOIUrl":"https://doi.org/10.1080/19475683.2022.2031293","url":null,"abstract":"ABSTRACT Rock music is an integral part of American culture. This paper presents a study of sensing and analysing over 57,000 rock music live performances between 2007 and 2017. Spatial traces of 575 rock music artists performing in concerts nationwide were collected from a major music streaming platform Spotify. Location-based concert data were analysed to explore economic and geographic factors linked to the landscape of rock music live performance and to reveal the importance of population demographics and leisure and hospitality (LH) economics to the culture and music industries from a spatial aspect. Over 90% of rock concerts between 2007 and 2017 were found in 250 counties. The aim of the study is to specify and develop a model that reasonably accounts for spatial heterogeneity present in the concert data. By regressing rock concert data against demographic data and LH establishment data, ordinary least squares (OLS) models were better fitted in metropolitan counties than non-metropolitan counties. Spatial dynamics of concerts were revealed by local R2 values and the obtained structure in the form of spatial heterogeneity was then explained using geographically weighted regression (GWR) models. High population density and LH services in industry-leading cities such as New York City, Los Angeles, Chicago and Houston exhibit advantages in explaining rock concert distributions. Findings from the models reflect the live music industry’s interrelationships to the LH industry and suggest LH services being essential considerations in selecting concert destinations for rock musicians.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"111 1","pages":"293 - 306"},"PeriodicalIF":5.0,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79625493","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}
ABSTRACT Studying the structure of polycentric cities can promote a better understanding of urban development and contribute to urban planning. In this study, we identified polycentric cities in China and evaluated the urban centre development level of polycentric cities from new data and method. We used Luojia-1A night-time light (NTL) data, combined with the concept of natural cities (NCs), to identify urban centres and thus identify polycentric cities in China. In addition, we used the urban centre development index (UCDI) to quantify the urban centre development level (UCDL) that represents the overall urban centre development level within a polycentric city. The polycentric cities in China are characterized by the spatial distribution pattern of a larger number in the east and fewer in the west. There are a large number of polycentric cities in eastern China, and the closer to the coastal areas, the more polycentric cities there are. The distribution of UCDL in China’s polycentric cities is characterized by significant spatial heterogeneity. UCDLs are generally smaller in polycentric cities in western China. In addition, polycentric cities in northeastern China have smaller UCDL. Polycentric cities with high UCDL are concentrated in the central and coastal regions of China.
{"title":"Identifying China’s polycentric cities and evaluating the urban centre development level using Luojia-1A night-time light data","authors":"Zhiwei Yang, Yingbiao Chen, Zihao Zheng, Zhi-feng Wu","doi":"10.1080/19475683.2022.2026472","DOIUrl":"https://doi.org/10.1080/19475683.2022.2026472","url":null,"abstract":"ABSTRACT Studying the structure of polycentric cities can promote a better understanding of urban development and contribute to urban planning. In this study, we identified polycentric cities in China and evaluated the urban centre development level of polycentric cities from new data and method. We used Luojia-1A night-time light (NTL) data, combined with the concept of natural cities (NCs), to identify urban centres and thus identify polycentric cities in China. In addition, we used the urban centre development index (UCDI) to quantify the urban centre development level (UCDL) that represents the overall urban centre development level within a polycentric city. The polycentric cities in China are characterized by the spatial distribution pattern of a larger number in the east and fewer in the west. There are a large number of polycentric cities in eastern China, and the closer to the coastal areas, the more polycentric cities there are. The distribution of UCDL in China’s polycentric cities is characterized by significant spatial heterogeneity. UCDLs are generally smaller in polycentric cities in western China. In addition, polycentric cities in northeastern China have smaller UCDL. Polycentric cities with high UCDL are concentrated in the central and coastal regions of China.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"19 1","pages":"185 - 195"},"PeriodicalIF":5.0,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74597799","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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