ISPRS Int. J. Geo Inf.最新文献_第2页

Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network 基于注意力增强时间卷积网络的车位可用性短期多尺度智能预测

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-22 DOI: 10.3390/ijgi12050208

Ke Shang, Zeyu Wan, Yulin Zhang, Zhiwei Cui, Zihan Zhang, Chenchen Jiang, Feizhou Zhang

{"title":"Intelligent Short-Term Multiscale Prediction of Parking Space Availability Using an Attention-Enhanced Temporal Convolutional Network","authors":"Ke Shang, Zeyu Wan, Yulin Zhang, Zhiwei Cui, Zihan Zhang, Chenchen Jiang, Feizhou Zhang","doi":"10.3390/ijgi12050208","DOIUrl":"https://doi.org/10.3390/ijgi12050208","url":null,"abstract":"The accurate and rapid prediction of parking availability is helpful for improving parking efficiency and to optimize traffic systems. However, previous studies have suffered from limited training sample sizes and a lack of thorough investigation into the correlations among the factors affecting parking availability. The purpose of this study is to explore a prediction method that can account for multiple factors. Firstly, a dynamic prediction method based on a temporal convolutional network (TCN) model was confirmed to be efficient for ultra-short-term parking availability with an accuracy of 0.96 MSE. Then, an attention-enhanced TCN (A-TCN) model based on spatial attention modules was proposed. This model integrates multiple factors, including related dates, extreme weather, and human control, to predict the daily congestion index of parking lots in the short term, with a prediction period of up to one month. Experimental results on real data demonstrate that the MSE of A-TCN is 0.0061, exhibiting better training efficiency and prediction accuracy than a traditional TCN for the short-term prediction time scale.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89969140","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}

引用次数: 0

STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis STO2Vec:一种面向关联分析的多尺度时空对象表示方法

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-21 DOI: 10.3390/ijgi12050207

Nanyu Chen, Anran Yang, Luo Chen, W. Xiong, Ning Jing

{"title":"STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis","authors":"Nanyu Chen, Anran Yang, Luo Chen, W. Xiong, Ning Jing","doi":"10.3390/ijgi12050207","DOIUrl":"https://doi.org/10.3390/ijgi12050207","url":null,"abstract":"Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83291933","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}

引用次数: 0

MAAFEU-Net: A Novel Land Use Classification Model Based on Mixed Attention Module and Adjustable Feature Enhancement Layer in Remote Sensing Images MAAFEU-Net:基于混合关注模块和可调特征增强层的遥感影像土地利用分类新模型

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-20 DOI: 10.3390/ijgi12050206

Yonghong Zhang, Huajun Zhao, Guangyi Ma, Don Xie, Sutong Geng, Huanyu Lu, Wei Tian, K. T. C. L. K. Sian

{"title":"MAAFEU-Net: A Novel Land Use Classification Model Based on Mixed Attention Module and Adjustable Feature Enhancement Layer in Remote Sensing Images","authors":"Yonghong Zhang, Huajun Zhao, Guangyi Ma, Don Xie, Sutong Geng, Huanyu Lu, Wei Tian, K. T. C. L. K. Sian","doi":"10.3390/ijgi12050206","DOIUrl":"https://doi.org/10.3390/ijgi12050206","url":null,"abstract":"The classification of land use information is important for land resource management. With the purpose of extracting precise spatial information, we present a novel land use classification model based on a mixed attention module and adjustable feature enhancement layer (MAAFEU-net). Our unique design, the mixed attention module, allows the model to concentrate on target-specific discriminative features and capture class-related features within different land use types. In addition, an adjustable feature enhancement layer is proposed to further enhance the classification ability of similar types. We assess the performance of this model using the publicly available GID dataset and the self-built Gwadar dataset. Six semantic segmentation deep networks are used for comparison. The experimental results show that the F1 score of MAAFEU-net is 2.16% and 2.3% higher than the next model and that MIoU is 3.15% and 3.62% higher than the next model. The results of the ablation experiments show that the mixed attention module improves the MIoU by 5.83% and the addition of the adjustable feature enhancement layer can further improve it by 5.58%. Both structures effectively improve the accuracy of the overall land use classification. The validation results show that MAAFEU-net can obtain land use classification images with high precision.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85447041","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}

引用次数: 0

Quantifying the Effect of Socio-Economic Predictors and the Built Environment on Mental Health Events in Little Rock, AR 量化社会经济预测因素和建筑环境对AR小石城心理健康事件的影响

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-18 DOI: 10.3390/ijgi12050205

Alfieri Ek, Grant Drawve, Samantha Robinson, Jyotishka Datta

{"title":"Quantifying the Effect of Socio-Economic Predictors and the Built Environment on Mental Health Events in Little Rock, AR","authors":"Alfieri Ek, Grant Drawve, Samantha Robinson, Jyotishka Datta","doi":"10.3390/ijgi12050205","DOIUrl":"https://doi.org/10.3390/ijgi12050205","url":null,"abstract":"Law enforcement agencies continue to grow in the use of spatial analysis to assist in identifying patterns of outcomes. Despite the critical nature of proper resource allocation for mental health incidents, there has been little progress in statistical modeling of the geo-spatial nature of mental health events in Little Rock, Arkansas. In this article, we provide insights into the spatial nature of mental health data from Little Rock, Arkansas between 2015 and 2018, under a supervised spatial modeling framework. We provide evidence of spatial clustering and identify the important features influencing such heterogeneity via a spatially informed hierarchy of generalized linear, tree-based, and spatial regression models, viz. the Poisson regression model, the random forest model, the spatial Durbin error model, and the Manski model. The insights obtained from these different models are presented here along with their relative predictive performances. The inferential tools developed here can be used in a broad variety of spatial modeling contexts and have the potential to aid both law enforcement agencies and the city in properly allocating resources. We were able to identify several built-environment and socio-demographic measures related to mental health calls while noting that the results indicated that there are unmeasured factors that contribute to the number of events.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78640267","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}

引用次数: 0

Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19 新型冠状病毒肺炎疫情前后天津市中心城区智能包裹柜空间分布变化分析

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-16 DOI: 10.3390/ijgi12050203

Mengyue Ding, Nadeem Ullah, Sara Grigoryan, Yike Hu, Yan Song

{"title":"Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19","authors":"Mengyue Ding, Nadeem Ullah, Sara Grigoryan, Yike Hu, Yan Song","doi":"10.3390/ijgi12050203","DOIUrl":"https://doi.org/10.3390/ijgi12050203","url":null,"abstract":"The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin’s central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin’s development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81179136","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}

引用次数: 1

Flexible Trip-Planning Queries 灵活的行程规划查询

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-16 DOI: 10.3390/ijgi12050204

Gloria Bordogna, P. Carrara, Luca Frigerio, Simone Lella

{"title":"Flexible Trip-Planning Queries","authors":"Gloria Bordogna, P. Carrara, Luca Frigerio, Simone Lella","doi":"10.3390/ijgi12050204","DOIUrl":"https://doi.org/10.3390/ijgi12050204","url":null,"abstract":"The current practice of users searching for different types of geo-resources in a geographic area and wishing to identify the most convenient routes for visiting the most relevant ones, requires the iterative formulation of several queries: first to identify the more interesting resources and then to select the best route to visit them. In order to simplify this process, in this paper a novel functionality for a geographic information retrieval (GIR) system is proposed, which retrieves and ranks several routes for visiting a number of relevant georeferenced resources as a result of a single query, named flexible trip-planning query. An original retrieval model is defined to identify the relevant resources and to rank the most convenient routes by taking into account personal user preferences. To this end, a graph-based algorithm is defined, exploiting prioritized aggregation to optimize the routes’ identification and ranking. The proposed algorithm is applied in the proof-of-concept of a Smart cOmmunity-based Geographic infoRmation rEtrievAl SysTem (SO-GREAT) designed to strengthen local communities: it collects and manages open data from regional authorities describing categories of authoritative territorial resources and services, such as schools, hospitals, etc., and from volunteered geographic services (VGSs) created by citizens to offer services in their neighbourhood.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76422945","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}

引用次数: 0

Crime Risk Analysis of Tangible Cultural Heritage in China from a Spatial Perspective 空间视角下的中国物质文化遗产犯罪风险分析

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-15 DOI: 10.3390/ijgi12050201

Ning Ding, Yiming Zhai, Hongyu Lv

{"title":"Crime Risk Analysis of Tangible Cultural Heritage in China from a Spatial Perspective","authors":"Ning Ding, Yiming Zhai, Hongyu Lv","doi":"10.3390/ijgi12050201","DOIUrl":"https://doi.org/10.3390/ijgi12050201","url":null,"abstract":"Tangible cultural heritage is vulnerable to various risks, particularly those stemming from criminal activity. Through analyzing the distribution and flow of crime risks from a spatial perspective based on quantitative methods, risks can be better managed to contribute to the protection of cultural heritage. This paper explores and summarizes the spatial characteristics of crime risks from 2011 to 2019 in China. Firstly, the average nearest neighbor (ANN) and the Jenks Natural Breaks Classification method showed that the national key protected heritage sites (NPS) and crime risks exhibit clustering features in space, and most of the NPS were located in the middle and lower reaches of the Yangtze River and the Yellow River. Secondly, the economy has no impact on crime risks in the spatial statistical analysis. However, the population density, distribution of NPS, and tourism development influenced specific types of crime risks. Finally, Global Moran’s I was used to examine the strong sensitivity between crime risks and cultural relics protection policies. The quantitative results of this study can be applied to improve strategies for crime risk prevention and the effectiveness of heritage security policy formulation.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84832586","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}

引用次数: 0

Isolated or Colocated? Exploring the Spatio-Temporal Evolution Pattern and Influencing Factors of the Attractiveness of Residential Areas to Restaurants in the Central Urban Area 孤立的还是共同的?中心城区住区对餐饮吸引力的时空演变规律及影响因素研究

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-15 DOI: 10.3390/ijgi12050202

Ruien Tang, Guolin Hou, Rui Du

{"title":"Isolated or Colocated? Exploring the Spatio-Temporal Evolution Pattern and Influencing Factors of the Attractiveness of Residential Areas to Restaurants in the Central Urban Area","authors":"Ruien Tang, Guolin Hou, Rui Du","doi":"10.3390/ijgi12050202","DOIUrl":"https://doi.org/10.3390/ijgi12050202","url":null,"abstract":"Catering and urban elements have a strong spatial association. The spatial clustering and dispersal patterns of catering can effectively influence cities’ economic and socio-spatial reconfiguration. This research first introduced the concept of the ARTR (the attractiveness of residential areas to restaurants) and measured its value as well as its spatial and temporal evolutionary patterns using global and local colocation quotients. The DBSCAN algorithm and spatial hot-spot analysis were used to analyze their spatial evolution patterns. On this basis, a multiscale geographically weighted regression (MGWR) model was used to analyze the scale of and spatial variation in the drivers. The results show that (1) Nanjing’s ARTR is at a low level, with the most significant decline in ARTR occurring from 2005 to 2020 for MRs and HRs, while LRs did not significantly respond to urban regeneration. (2) The spatial layout of the ARTR in Nanjing has gradually evolved from a circular structure to a semi-enclosed structure, and the circular structure has continued to expand outward. At the same time, the ARTR for different levels of catering shows a diverse distribution in the margins. (3) Urban expansion and regeneration have led to increasingly negative effects of the clustering level, commercial competition, economic level and neighborhood newness, while the density of the road network has been more stable. (4) The road network density has consistently remained a global influence. Commercial diversity has changed from a local factor to a global factor, while economic and locational factors have strongly spatially non-smooth relationships with the ARTR. The results of this study can provide a basis for a harmonious relationship between catering and residential areas in the context of urban expansion and regeneration.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78114790","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}

引用次数: 0

A Survey of Methods and Input Data Types for House Price Prediction 房价预测方法与输入数据类型综述

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-14 DOI: 10.3390/ijgi12050200

M. Geerts, S. V. Broucke, Jochen De Weerdt

{"title":"A Survey of Methods and Input Data Types for House Price Prediction","authors":"M. Geerts, S. V. Broucke, Jochen De Weerdt","doi":"10.3390/ijgi12050200","DOIUrl":"https://doi.org/10.3390/ijgi12050200","url":null,"abstract":"Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021 presenting a particular technique for house price prediction. Subsequently, we scrutinized these works and scored them according to model and data novelty. A cluster analysis allowed mapping of the property valuation domain and identification of trends. Although conventional methods and traditional input data remain predominant, house price prediction research is slowly adopting more advanced techniques and innovative data sources. In addition, we identify opportunities to include more advanced input data types such as unstructured data and complex spatial data and to introduce deep learning and tailored methods, which could guide further research.","PeriodicalId":14614,"journal":{"name":"ISPRS Int. J. Geo Inf.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81459777","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}

引用次数: 1

Efficient Management and Scheduling of Massive Remote Sensing Image Datasets 海量遥感影像数据集的高效管理与调度

ISPRS Int. J. Geo Inf. Pub Date : 2023-05-13 DOI: 10.3390/ijgi12050199

Jiankun Zhu, Zhen Zhang, Fei Zhao, Haoran Su, Zhengnan Gu, Leilei Wang

引用次数: 0