Pub Date : 2020-12-08DOI: 10.1080/19475683.2020.1848920
V. Kalichkin, A. I. Pavlova, O. Logachova
ABSTRACT The main purpose of this work is to assess the suitability of land for cultivation of the main agricultural crop of Western Siberia, namely spring wheat. The algorithm of land suitability assessment was developed on the territory of the test plot of land-use of CJSC Mirny, Kochenevsky District, Novosibirsk Region. For assessment of land suitability on the basis of expert knowledge, criteria related to relief and soil, not only known but also specific, inherent in the area under consideration, have been identified. In the absence of information on the topography and relief of the territory under consideration, the spatial database of geodata was created based on the results of high-resolution digital aerial photography from an unmanned aerial vehicle (UAV). Elementary surfaces (ESs) at the micro-relief level have been determined with the help of GIS tools. Two most popular methods of obtaining criterion weights have been analysed: Analytic Hierarchy Process and the direct ranking method, and under certain conditions, a connection between these methods have been established. To assess the land suitability, the land suitability indices of selected ESs were calculated using GIS-MCDA (Multiple-Criteria Decision Analysis) method Weighted linear combination. Based on the value of land suitability index for all ESs, belonging to a certain suitability class according to FAO classification has been established. A map of land suitability with an assessment of spring wheat sowing expediency was obtained.
{"title":"GIS-BASED MULTI-CRITERIA ANALYSIS OF THE SUITABILITY OF WESTERN SIBERIAN FOREST-STEPPE LANDS","authors":"V. Kalichkin, A. I. Pavlova, O. Logachova","doi":"10.1080/19475683.2020.1848920","DOIUrl":"https://doi.org/10.1080/19475683.2020.1848920","url":null,"abstract":"ABSTRACT The main purpose of this work is to assess the suitability of land for cultivation of the main agricultural crop of Western Siberia, namely spring wheat. The algorithm of land suitability assessment was developed on the territory of the test plot of land-use of CJSC Mirny, Kochenevsky District, Novosibirsk Region. For assessment of land suitability on the basis of expert knowledge, criteria related to relief and soil, not only known but also specific, inherent in the area under consideration, have been identified. In the absence of information on the topography and relief of the territory under consideration, the spatial database of geodata was created based on the results of high-resolution digital aerial photography from an unmanned aerial vehicle (UAV). Elementary surfaces (ESs) at the micro-relief level have been determined with the help of GIS tools. Two most popular methods of obtaining criterion weights have been analysed: Analytic Hierarchy Process and the direct ranking method, and under certain conditions, a connection between these methods have been established. To assess the land suitability, the land suitability indices of selected ESs were calculated using GIS-MCDA (Multiple-Criteria Decision Analysis) method Weighted linear combination. Based on the value of land suitability index for all ESs, belonging to a certain suitability class according to FAO classification has been established. A map of land suitability with an assessment of spring wheat sowing expediency was obtained.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"158 1","pages":"225 - 237"},"PeriodicalIF":5.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86729441","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 : 2020-11-24DOI: 10.1080/19475683.2021.1936172
J. Beairsto, Yufan Tian, Linyu Zheng, Qunshan Zhao, Jinhyun Hong
ABSTRACT Worldwide bike-sharing systems are growing in popularity as an alternative, environmentally friendly mode of transportation. As cities seek to further develop bike-sharing programmes, it is important to consider how systems should expand to simultaneously address existing inequalities in accessibility, and best serve demand. In this paper, we determine ideal locations for future bike-sharing stations in Glasgow, Scotland, by integrating demand modelling with accessibility considerations. We began by analysing the spatio-temporal trends of bike-sharing usage, and assessed the spatial equity of access to stations in Glasgow. To identify important determinants of bike-sharing demand, we ran an ordinary least squares regression model using bike sharing trip data from Nextbike Glasgow. We then quantifiably measured the level of spatial accessibility to stations by applying the two-step floating catchment area (2SFCA) methodology and ran a GIS weighted overlay analysis using the significant determinants of station demand. Lastly, we combined the demand and accessibility results to determine where new stations should be located using a maximum covering location problem (MCLP) that maximized the population served. Our results show that distance from transit stations, distance from downtown, employment rates, and nearby cycling lanes are significant factors affecting station-level demand. Furthermore, levels of spatial access were found to be highest primarily in the centre and eastern neighbourhood of Glasgow. These findings aided in determining areas to prioritize for future station locations, and our methodology can easily be applied to other bike-share programmes with adjustments according to varying aims for system expansion.
{"title":"Identifying locations for new bike-sharing stations in Glasgow: an analysis of spatial equity and demand factors","authors":"J. Beairsto, Yufan Tian, Linyu Zheng, Qunshan Zhao, Jinhyun Hong","doi":"10.1080/19475683.2021.1936172","DOIUrl":"https://doi.org/10.1080/19475683.2021.1936172","url":null,"abstract":"ABSTRACT Worldwide bike-sharing systems are growing in popularity as an alternative, environmentally friendly mode of transportation. As cities seek to further develop bike-sharing programmes, it is important to consider how systems should expand to simultaneously address existing inequalities in accessibility, and best serve demand. In this paper, we determine ideal locations for future bike-sharing stations in Glasgow, Scotland, by integrating demand modelling with accessibility considerations. We began by analysing the spatio-temporal trends of bike-sharing usage, and assessed the spatial equity of access to stations in Glasgow. To identify important determinants of bike-sharing demand, we ran an ordinary least squares regression model using bike sharing trip data from Nextbike Glasgow. We then quantifiably measured the level of spatial accessibility to stations by applying the two-step floating catchment area (2SFCA) methodology and ran a GIS weighted overlay analysis using the significant determinants of station demand. Lastly, we combined the demand and accessibility results to determine where new stations should be located using a maximum covering location problem (MCLP) that maximized the population served. Our results show that distance from transit stations, distance from downtown, employment rates, and nearby cycling lanes are significant factors affecting station-level demand. Furthermore, levels of spatial access were found to be highest primarily in the centre and eastern neighbourhood of Glasgow. These findings aided in determining areas to prioritize for future station locations, and our methodology can easily be applied to other bike-share programmes with adjustments according to varying aims for system expansion.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"35 1","pages":"111 - 126"},"PeriodicalIF":5.0,"publicationDate":"2020-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90851994","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 : 2020-11-18DOI: 10.1080/19475683.2020.1848921
B. Mashhoodi, N. H. V. D. Blij
ABSTRACT Due to the sharp growth in the adaptation of electric vehicles (EV) in the Netherlands and the objectives of the Dutch Climate Accord is to encourage electric mobility, in the coming decades a substantial number of new EV charging facilities needs to be provided. Efficient planning of EV charging infrastructure is coupled with the notion of range anxiety, which is likely to be severely high in case of soon-to-be EV drivers. This study aims to estimate the cost of developing a new charging infrastructure under five scenarios of range anxiety in Amsterdam East. Employing a Linear Integer Programming optimization model, on the basis of geographic data on car registration, existing EV chargers, and electricity substations, it is obtained that if drivers use 90% of their battery before using a charging facility, the existing charging infrastructure needs to be expanded by only 31% to accommodate almost seven times larger number of EVs – the threshold set by the European Union (EU) legislation on the deployment of alternative fuel infrastructure. If drivers use only 30% of the batteries; however, an increase of 167% in infrastructure is inevitable (accounting for almost five million euro of cost). Second, at any point along the range anxiety spectrum, if the interval between charging session increases for 1 day, the overall cost decreases by more than 30%. These findings are discussed, and two policy approaches are proposed: (1) information technology approach; (2) demand-response approach, on the basis of EU legislation on energy efficiency and deployment of alternative fuel infrastructure.
{"title":"Drivers’ range anxiety and cost of new EV chargers in Amsterdam: a scenario-based optimization approach","authors":"B. Mashhoodi, N. H. V. D. Blij","doi":"10.1080/19475683.2020.1848921","DOIUrl":"https://doi.org/10.1080/19475683.2020.1848921","url":null,"abstract":"ABSTRACT Due to the sharp growth in the adaptation of electric vehicles (EV) in the Netherlands and the objectives of the Dutch Climate Accord is to encourage electric mobility, in the coming decades a substantial number of new EV charging facilities needs to be provided. Efficient planning of EV charging infrastructure is coupled with the notion of range anxiety, which is likely to be severely high in case of soon-to-be EV drivers. This study aims to estimate the cost of developing a new charging infrastructure under five scenarios of range anxiety in Amsterdam East. Employing a Linear Integer Programming optimization model, on the basis of geographic data on car registration, existing EV chargers, and electricity substations, it is obtained that if drivers use 90% of their battery before using a charging facility, the existing charging infrastructure needs to be expanded by only 31% to accommodate almost seven times larger number of EVs – the threshold set by the European Union (EU) legislation on the deployment of alternative fuel infrastructure. If drivers use only 30% of the batteries; however, an increase of 167% in infrastructure is inevitable (accounting for almost five million euro of cost). Second, at any point along the range anxiety spectrum, if the interval between charging session increases for 1 day, the overall cost decreases by more than 30%. These findings are discussed, and two policy approaches are proposed: (1) information technology approach; (2) demand-response approach, on the basis of EU legislation on energy efficiency and deployment of alternative fuel infrastructure.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"124 1","pages":"87 - 98"},"PeriodicalIF":5.0,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87857497","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 : 2020-11-11DOI: 10.1080/19475683.2020.1841828
T. E. Chow, Yusik Choi, Mei Yang, D. Mills, R. Yue
ABSTRACT This research investigates how travel restrictions affect the spatiotemporal pattern of human mobility and COVID-19 confirmed cases. Based on recorded movement and Baidu mobility index, in- and out-migration were estimated to examine the geographic pattern of human mobility across many Chinese cities from Jan 1 – Feb 11 of 2020. In addition to the baseline model of city lockdown , this study also explored the time lag effect of COVID-19 incubation period before/after Jan 28 (i.e. 5 days) and Feb 6 (i.e. 2 weeks) as well. Full factorial Analysis of Variance (ANOVA) tests reviewed significant differences of migration pattern by lockdown and origin/destination, which are also significantly associated with the confirmed cases of COVID-19 as well. Specifically, human mobility dropped proportionally after the lockdown regardless of origin location, but Hubei destination was significantly lower than non-Hubei destination. The model assuming an incubation period of 5 days differentiated the differences of COVID-19 cases better than the baseline and 14 days model. Spatiotemporal cluster analysis identified multiple space-time windows that were related to migration trajectory assuming a 5–14 days incubation period. The pre-lockdown clusters due to traveler’s outflow from Wuhan to those megacities were the pathways for international transmission of COVID-19, whereas the post-lockdown clusters were partially related to the migration pattern especially within the eastern part of Hubei around Wuhan. The geographic pattern revealed from this study confirmed the presence of super spreaders that were responsible for regional spreading at the early stage and caused local outbreaks in the latter stage.
{"title":"Geographic pattern of human mobility and COVID-19 before and after Hubei lockdown","authors":"T. E. Chow, Yusik Choi, Mei Yang, D. Mills, R. Yue","doi":"10.1080/19475683.2020.1841828","DOIUrl":"https://doi.org/10.1080/19475683.2020.1841828","url":null,"abstract":"ABSTRACT This research investigates how travel restrictions affect the spatiotemporal pattern of human mobility and COVID-19 confirmed cases. Based on recorded movement and Baidu mobility index, in- and out-migration were estimated to examine the geographic pattern of human mobility across many Chinese cities from Jan 1 – Feb 11 of 2020. In addition to the baseline model of city lockdown , this study also explored the time lag effect of COVID-19 incubation period before/after Jan 28 (i.e. 5 days) and Feb 6 (i.e. 2 weeks) as well. Full factorial Analysis of Variance (ANOVA) tests reviewed significant differences of migration pattern by lockdown and origin/destination, which are also significantly associated with the confirmed cases of COVID-19 as well. Specifically, human mobility dropped proportionally after the lockdown regardless of origin location, but Hubei destination was significantly lower than non-Hubei destination. The model assuming an incubation period of 5 days differentiated the differences of COVID-19 cases better than the baseline and 14 days model. Spatiotemporal cluster analysis identified multiple space-time windows that were related to migration trajectory assuming a 5–14 days incubation period. The pre-lockdown clusters due to traveler’s outflow from Wuhan to those megacities were the pathways for international transmission of COVID-19, whereas the post-lockdown clusters were partially related to the migration pattern especially within the eastern part of Hubei around Wuhan. The geographic pattern revealed from this study confirmed the presence of super spreaders that were responsible for regional spreading at the early stage and caused local outbreaks in the latter stage.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"47 1","pages":"127 - 138"},"PeriodicalIF":5.0,"publicationDate":"2020-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90906089","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 : 2020-11-04DOI: 10.1080/19475683.2020.1840434
Danial Alizadeh, A. Alesheikh, M. Sharif
ABSTRACT Maritime traffic prediction is a crucial task for increasing the efficiency of port operations and safety, especially in congested regions. A huge amount of automatic identification system (AIS) data is constantly transmitting from vessels to receivers that contain information about vessels’ movements and characteristics. These historical AIS data can be utilized in movement analyses of vessels. This paper proposes a novel point-based model for location and traffic prediction using vessels’ trajectories adapted from AIS measures. The location prediction procedure is setup based on similarity analysis of historical AIS data. The model is applied to a real dataset of hundreds of vessels’ trajectories in the Strait of Georgia, USA. The correlation results of 0.9976, 0.9887, and 0.9794 for the next 10, 20, and 30 minutes, respectively, imply sufficient correspondence between predicted and actual coordinates. The traffic prediction procedure considers the probability of the appearance of new vessels inside an area of interest (AoI) at different time intervals. The Sorenson similarity index (SSI) is used to measure the accuracy of the traffic prediction model. The SSIs for time intervals of 10, 20, and 30 minutes are 70%, 66%, and 59%, respectively, which show the robustness of the model to predict hot spots inside the AoI.
{"title":"Prediction of vessels locations and maritime traffic using similarity measurement of trajectory","authors":"Danial Alizadeh, A. Alesheikh, M. Sharif","doi":"10.1080/19475683.2020.1840434","DOIUrl":"https://doi.org/10.1080/19475683.2020.1840434","url":null,"abstract":"ABSTRACT Maritime traffic prediction is a crucial task for increasing the efficiency of port operations and safety, especially in congested regions. A huge amount of automatic identification system (AIS) data is constantly transmitting from vessels to receivers that contain information about vessels’ movements and characteristics. These historical AIS data can be utilized in movement analyses of vessels. This paper proposes a novel point-based model for location and traffic prediction using vessels’ trajectories adapted from AIS measures. The location prediction procedure is setup based on similarity analysis of historical AIS data. The model is applied to a real dataset of hundreds of vessels’ trajectories in the Strait of Georgia, USA. The correlation results of 0.9976, 0.9887, and 0.9794 for the next 10, 20, and 30 minutes, respectively, imply sufficient correspondence between predicted and actual coordinates. The traffic prediction procedure considers the probability of the appearance of new vessels inside an area of interest (AoI) at different time intervals. The Sorenson similarity index (SSI) is used to measure the accuracy of the traffic prediction model. The SSIs for time intervals of 10, 20, and 30 minutes are 70%, 66%, and 59%, respectively, which show the robustness of the model to predict hot spots inside the AoI.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"61 1","pages":"151 - 162"},"PeriodicalIF":5.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84483556","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 : 2020-10-22DOI: 10.1080/19475683.2020.1829704
Ruowei Liu, X. Yao, Chenxiao Guo, Xuebin Wei
ABSTRACT Forecasting political elections has attracted a lot of attention. Traditional election forecasting models in political science generally take preference in poll surveys and economic growth at the national level as the predictive factors. However, spatially or temporally dense polling has always been expensive. In the recent decades, the exponential growth of social media has drawn enormous research interests from various disciplines. Existing studies suggest that social media data have the potential to reflect the political landscape. Particularly, Twitter data have been extensively used for sentiment analysis to predict election outcomes around the world. However, previous studies have typically been data-driven and the reasoning process was oversimplified without robust theoretical foundations. Most of the studies correlate twitter sentiment directly and solely with the election results which can hardly be regarded as predictions. To develop a more theoretically plausible approach this study draws on political science prediction models and modifies them in two aspects. First, our approach uses Twitter sentiment to replace polling data. Second, we transform traditional political science models from the national level to the county level, the finest spatial level of voting counts. The proposed model has independent variables of support rate based on Twitter sentiment and variables related to economic growth. The dependent variable is the actual voting result. The 2016 U.S. presidential election data in Georgia is used to train the model. Results show that the proposed modely is effective with the accuracy of 81% and the support rate based on Twitter sentiment ranks the second most important feature.
{"title":"Can We Forecast Presidential Election Using Twitter Data? An Integrative Modelling Approach","authors":"Ruowei Liu, X. Yao, Chenxiao Guo, Xuebin Wei","doi":"10.1080/19475683.2020.1829704","DOIUrl":"https://doi.org/10.1080/19475683.2020.1829704","url":null,"abstract":"ABSTRACT Forecasting political elections has attracted a lot of attention. Traditional election forecasting models in political science generally take preference in poll surveys and economic growth at the national level as the predictive factors. However, spatially or temporally dense polling has always been expensive. In the recent decades, the exponential growth of social media has drawn enormous research interests from various disciplines. Existing studies suggest that social media data have the potential to reflect the political landscape. Particularly, Twitter data have been extensively used for sentiment analysis to predict election outcomes around the world. However, previous studies have typically been data-driven and the reasoning process was oversimplified without robust theoretical foundations. Most of the studies correlate twitter sentiment directly and solely with the election results which can hardly be regarded as predictions. To develop a more theoretically plausible approach this study draws on political science prediction models and modifies them in two aspects. First, our approach uses Twitter sentiment to replace polling data. Second, we transform traditional political science models from the national level to the county level, the finest spatial level of voting counts. The proposed model has independent variables of support rate based on Twitter sentiment and variables related to economic growth. The dependent variable is the actual voting result. The 2016 U.S. presidential election data in Georgia is used to train the model. Results show that the proposed modely is effective with the accuracy of 81% and the support rate based on Twitter sentiment ranks the second most important feature.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"125 1","pages":"43 - 56"},"PeriodicalIF":5.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76326472","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 : 2020-10-20DOI: 10.1080/19475683.2020.1831603
Diego J. Maldonado-Guzmán
ABSTRACT The existence of works proving the possible relationship empirically that Airbnb lodgings could have with crime in Spain is not known. This research analyzes the relationship between Airbnb lodgings and crimes against the properties and people in Barcelona’s neighbourhoods. To achieve this, we use an ordinary least squares regression model and a geographically weighted regression model. The results show a significant and positive relationship between the higher density of Airbnb lodgings and the higher crime rates in the neighbourhoods, especially of patrimonial nature. Divided by type of leased space, the Airbnb homes, in which the guest shares a room with other guests, show a higher relationship with crimes against property and people. The results of the local model show a spatial heterogeneity in all variables used, indicating the need to address non-stationary spatial processes that reveal hidden patterns. However, the only variable that shows statistically significant local variability is the total Airbnb lodgings variable. Finally, we discussed some unexpected results, proposing some future lines of research.
{"title":"Airbnb and crime in Barcelona (Spain): testing the relationship using a geographically weighted regression","authors":"Diego J. Maldonado-Guzmán","doi":"10.1080/19475683.2020.1831603","DOIUrl":"https://doi.org/10.1080/19475683.2020.1831603","url":null,"abstract":"ABSTRACT The existence of works proving the possible relationship empirically that Airbnb lodgings could have with crime in Spain is not known. This research analyzes the relationship between Airbnb lodgings and crimes against the properties and people in Barcelona’s neighbourhoods. To achieve this, we use an ordinary least squares regression model and a geographically weighted regression model. The results show a significant and positive relationship between the higher density of Airbnb lodgings and the higher crime rates in the neighbourhoods, especially of patrimonial nature. Divided by type of leased space, the Airbnb homes, in which the guest shares a room with other guests, show a higher relationship with crimes against property and people. The results of the local model show a spatial heterogeneity in all variables used, indicating the need to address non-stationary spatial processes that reveal hidden patterns. However, the only variable that shows statistically significant local variability is the total Airbnb lodgings variable. Finally, we discussed some unexpected results, proposing some future lines of research.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"19 1","pages":"147 - 160"},"PeriodicalIF":5.0,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73543054","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 : 2020-10-09DOI: 10.1080/19475683.2020.1817146
Jirapa Vongkusolkit, Qunying Huang
ABSTRACT Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.
{"title":"Situational awareness extraction: a comprehensive review of social media data classification during natural hazards","authors":"Jirapa Vongkusolkit, Qunying Huang","doi":"10.1080/19475683.2020.1817146","DOIUrl":"https://doi.org/10.1080/19475683.2020.1817146","url":null,"abstract":"ABSTRACT Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"1 1","pages":"5 - 28"},"PeriodicalIF":5.0,"publicationDate":"2020-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90682916","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 Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area.
{"title":"Analysis of shoreline changes in Vishakhapatnam coastal tract of Andhra Pradesh, India: an application of digital shoreline analysis system (DSAS)","authors":"Mirza Razi Imam Baig, Ishita Afreen Ahmad, Shahfahad, M. Tayyab, Atiqur Rahman","doi":"10.1080/19475683.2020.1815839","DOIUrl":"https://doi.org/10.1080/19475683.2020.1815839","url":null,"abstract":"ABSTRACT Coastline or Shoreline calculation is one of the important factors in the finding of coastal accretion and erosion and the study of coastal morphodynamic. Coastal erosion is a tentative hazard for communities especially in coastal areas as it is extremely susceptible to increasing coastal disasters. The study has been conducted along the coast of Vishakhapatnam district, Andhra Pradesh, India with the help of multi-temporal satellite images of 1991 2001, 2011 and 2018. The continuing coastal erosion and accretion rates have been calculated using the Digital Shoreline Analysis System (DSAS). Linear regression rate (LRR), End Point Rate (EPR) and Weighted Linear Regression (WLR) are used for calculating shoreline change rate. Based on calculations the district shoreline has been classified into five categories as high and low erosion, no change and high and low accretion. Out of 135 km, high erosion occupied 5.8 km of coast followed by moderate or low erosion 46.2 km. Almost 34.7 km coastal length showed little or no change. Moderate accretion is found along 30.5 km whereas high accretion trend found around 17.8 km. The outcome of shows that erosion is prevailing in Vishakhapatnam taluk, Ankapalli taluk, Yellamanchili taluk whereas most of the Bhemunipatnam coast is accreting. Natural and manmade activities and phenomena influence the coastal areas in terms of erosion and accretion. The study could be used for further planning and development and also for disaster management authority in the decision-making process in the study area.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"36 1","pages":"361 - 376"},"PeriodicalIF":5.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77831386","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 : 2020-10-01DOI: 10.1080/19475683.2020.1833365
Ran Tao, J. Downs, Theresa M. Beckie, Yuzhou Chen, W. McNelley
ABSTRACT Massive and rapid testing is crucial for containing the spread of COVID-19. Health and policy planners must ensure that access to and uptake of SARS-CoV-2 testing is adequate and equitable. This study measures the spatial accessibility to testing sites in Florida at the census tract level at the end of May 2020, using the 2-step floating catchment area method that integrates both driving and walking modes. Accessibility scores were found to be heterogeneous across geographic regions and among different groups of people. In particular, many rural areas were in a testing desert. While people in larger cities tended to have better accessibility to testing, many did not have adequate accessibility at that time due to both capacity limitations and spatial factors. In particular, people without access to private vehicles and the elderly faced disadvantages in accessibility to testing sites even in urban areas. However, Black and low-income groups were disproportionally concentrated in neighbourhoods with above-average accessibility due to their closer proximity to testing sites. These results suggest that increased efforts are needed to reach vulnerable populations, including the elderly and those without private vehicles.
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