Annals of GIS最新文献

{"title":"Introduction to advancements of GIS in the new IT era","authors":"Weiwei Song, Changshan Wu","doi":"10.1080/19475683.2021.1890920","DOIUrl":"https://doi.org/10.1080/19475683.2021.1890920","url":null,"abstract":"","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"122 1","pages":"1 - 4"},"PeriodicalIF":5.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87971396","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}

{"title":"Geospatial analysis of malaria mortality in the kintampo health and demographic surveillance area of central Ghana","authors":"K. Wiru, Felix Boakye Oppong, Stephaney Gyaase, Oscar Agyei, S. Abubakari, S. Amenga‐Etego, Charles Zandoh, Kwaku Poku Asante","doi":"10.1080/19475683.2020.1853231","DOIUrl":"https://doi.org/10.1080/19475683.2020.1853231","url":null,"abstract":"ABSTRACT Malaria remains a menace to the existence of humanity in most contexts. Geospatial analysis of malaria mortality is crucial to identifying clusters of high disease burden and areas with limited access to malaria care for targeted control and remedial interventions. This study identified spatial and space-time clusters of malaria mortality in the Kintampo area of central Ghana. We used 1301 malaria deaths archived from 2005 to 2017 and Global Positioning System (GPS) point locations of the sub-districts in which these deaths occurred for our analysis. Mortality risks were smoothed and mapped using the Spatial Empirical Bayesian smoothing technique in Geoda (version 1.12.1.161) whereas spatial and spatio-temporal clustering analysis was done using SaTScan (version 9.6). Malaria mortality risks ranged between 1.2 and 2.4 deaths per 1000 population for persons of all ages and between 3.3 and 6.0 deaths per 1000 population for children under five years of age by sub-district. Two spatial clusters were detected for all-age malaria mortality with only the primary cluster (RR = 1.42, p = 0.001) being statistically significant. Also, two statistically significant space-time clusters were detected for all-age malaria mortality in the study area. The most likely cluster occurred between 2006 and 2011 in five sub-districts with a relative risk of 2.12 (p < 0.001) whilst the secondary cluster which had a relative risk of 2.47 (p < 0.001) occurred between 2005 and 2010 in four sub-districts. Similarly, only the most likely spatial cluster of under-five malaria mortality was statistically significant (RR = 1.36, p = 0.024). Furthermore, three spatio-temporal clusters of under-five malaria mortality were detected in the study area. The primary and second secondary clusters were statistically significant whilst the first secondary cluster had borderline significance. The primary cluster (RR = 4.49, p = 0.002) occurred in two sub-districts between 2006 and 2007. The first secondary cluster (RR = 2.21, P = 0.005) covered four sub-districts and was detected between 2006 and 2011 whereas the second secondary cluster (RR = 2.51, p = 0.003) covered two sub-districts between 2008 and 2013. Ultimately, our analysis identified a number of substantial spatial and apace-time clusters of malaria mortality in the study context, which could aid in the strategic planning, implementation and monitoring of targeted malaria control interventions.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"3 1","pages":"139 - 149"},"PeriodicalIF":5.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84216484","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}