Urban informatics最新文献

{"title":"Data science for pedestrian and high street retailing as a framework for advancing urban informatics to individual scales.","authors":"Paul M Torrens","doi":"10.1007/s44212-022-00009-x","DOIUrl":"10.1007/s44212-022-00009-x","url":null,"abstract":"<p><strong>Background: </strong>In this paper, we consider the applicability of the customer journey framework from retailing as a driver for urban informatics at individual scales within urban science. The customer journey considers shopper experiences in the context of shopping paths, retail service spaces, and touch-points that draw them into contact. Around this framework, retailers have developed sophisticated data science for observation, identification, and measurement of customers in the context of their shopping behavior. This knowledge supports broad data-driven understanding of customer experiences in physical spaces, economic spaces of decision and choice, persuasive spaces of advertising and branding, and inter-personal spaces of customer-staff interaction.</p><p><strong>Method: </strong>We review the literature on pedestrian and high street retailing, and on urban informatics. We investigate whether the customer journey could be usefully repurposed for urban applications. Specifically, we explore the potential use of the customer journey framework for producing new insight into pedestrian behavior, where a sort of empirical hyperopia has long abounded because data are always in short supply.</p><p><strong>Results: </strong>Our review addresses how the customer journey might be used as a structure for examining how urban walkers come into contact with the built environment, how people actively and passively sense and perceive ambient city life as they move, how pedestrians make sense of urban context, and how they use this knowledge to build cognition of city streetscapes. Each of these topics has relevance to walking studies specifically, but also to urban science more generally. We consider how retailing might reciprocally benefit from urban science perspectives, especially in extending the reach of retailers' insight beyond store walls, into the retail high streets from which they draw custom.</p><p><strong>Conclusion: </strong>We conclude that a broad set of theoretical frameworks, data collection schemes, and analytical methodologies that have advanced retail data science closer and closer to individual-level acumen might be usefully applied to accomplish the same in urban informatics. However, we caution that differences between retailers' and urban scientists' viewpoints on privacy presents potential controversy.</p>","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":" ","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9527144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33497868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using unstable data from mobile phone applications to examine recent trajectories of retail centre recovery.","authors":"Patrick Ballantyne, Alex Singleton, Les Dolega","doi":"10.1007/s44212-022-00022-0","DOIUrl":"10.1007/s44212-022-00022-0","url":null,"abstract":"<p><p>The COVID-19 pandemic has changed the ways in which we shop, with significant impacts on retail and consumption spaces. Yet, empirical evidence of these impacts, specifically at the national level, or focusing on latter periods of the pandemic remain notably absent. Using a large spatio-temporal mobility dataset, which exhibits significant temporal instability, we explore the recovery of retail centres from summer 2021 to 2022, considering in particular how these responses are determined by the <i>functional</i> and <i>structural</i> characteristics of retail centres and their regional geography. Our findings provide important empirical evidence of the multidimensionality of retail centre recovery, highlighting in particular the importance of composition, e-resilience and catchment deprivation in determining such trajectories, and identifying key retail centre <i>functions</i> and regions that appear to be recovering faster than others<i>.</i> In addition, we present a use case for mobility data that exhibits temporal stability, highlighting the benefits of viewing mobility data as a series of snapshots rather than a complete time series. It is our view that such data, when controlling for temporal stability, can provide a useful way to monitor the economic performance of retail centres over time, providing evidence that can inform policy decisions, and support interventions to both acute and longer-term issues in the retail sector.</p>","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":"21"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10437434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics","authors":"W. Yao, Jianwei Wu","doi":"10.1007/978-981-15-8983-6_22","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_22","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51107719","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":"User-Generated Content and Its Applications in Urban Studies","authors":"Wei Tu, Qingquan Li, Yatao Zhang, Y. Yue","doi":"10.1007/978-981-15-8983-6_29","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_29","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51107946","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":"Cultivating Urban Big Data","authors":"N. Xiao, H. Miller","doi":"10.1007/978-981-15-8983-6_31","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_31","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51108154","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":"Urban IoT: Advances, Challenges, and Opportunities for Mass Data Collection, Analysis, and Visualization","authors":"A. Hudson-Smith, Duncan Wilson, Steven Gray, Oliver Dawkins","doi":"10.1007/978-981-15-8983-6_38","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_38","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51108586","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":"Cloud, Edge, and Mobile Computing for Smart Cities","authors":"Qian Liu, Juan Gu, Jingchao Yang, Yun Li, D. Sha, Meng Xu, Ishan Shams, Manzhu Yu, C. Yang","doi":"10.1007/978-981-15-8983-6_41","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_41","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51108640","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":"AI and Deep Learning for Urban Computing","authors":"Senzhang Wang, Jiannong Cao","doi":"10.1007/978-981-15-8983-6_43","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_43","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51108646","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":"Cellular Automata Modeling for Urban and Regional Planning","authors":"A. Yeh, Xia Li, Chang Xia","doi":"10.1007/978-981-15-8983-6_45","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_45","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51108690","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":"Introduction to Urban Systems and Applications","authors":"M. Kwan","doi":"10.1007/978-981-15-8983-6_10","DOIUrl":"https://doi.org/10.1007/978-981-15-8983-6_10","url":null,"abstract":"","PeriodicalId":75283,"journal":{"name":"Urban informatics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"51105432","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}