Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities最新文献

In-Database Geospatial Analytics using Python 使用Python的数据库内地理空间分析

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365598

Avipsa Roy, Edouard Fouché, Rafael Rodriguez Morales, Gregor Möhler

{"title":"In-Database Geospatial Analytics using Python","authors":"Avipsa Roy, Edouard Fouché, Rafael Rodriguez Morales, Gregor Möhler","doi":"10.1145/3356395.3365598","DOIUrl":"https://doi.org/10.1145/3356395.3365598","url":null,"abstract":"The amount of spatial data acquired from crowdsourced platforms, mobile devices, sensors and cartographic agencies has grown exponentially over the past few years. Nearly half of the spatial data available currently are stored and processed through large relational databases. Due to a lack of generic open source tools, researchers and analysts often have difficulty in extracting and analyzing large amounts of spatial data from traditional databases. In order to overcome this challenge, the most effective way is to perform the analysis directly in the database, which enables quick retrieval and visualization of spatial data stored in relational databases. Also, working in-database reduces the network overhead, as users do not need to replicate the complete data into their local system. While a number of spatial analysis libraries are readily available, they do not work in-database, and typically require additional platform-specific software. Our goal is to bridge this gap by developing a new method through an open source software to perform fast and seamless spatial analysis without having to store the data in-memory. We propose a framework implemented in Python, which embeds geospatial analytics into a spatial database (i.e. IBM DB2 ®). The framework internally translates the spatial functions written by the user into SQL queries, which follow the standards of Open Geospatial Consortium (OGC) and can operate on single as well as multiple geometries. We then demonstrate how to combine the results of spatial operations with visualization methods such as choropleth maps within Jupyter notebooks. Finally, we elaborate upon the benefits of our approach via a real-world use case, in which we analyze crime hotspots in New York City using the in-database spatial functions.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064993","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: 3

ADMSv2

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365544

Chrysovalantis Anastasiou, Jianfa Lin, Chao He, Yao-Yi Chiang, Cyrus Shahabi

引用次数: 10

Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring 基于语义的地球观测数据时空建模:在洪水监测中的应用

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365545

Kuldeep R. Kurte, Abhishek V. Potnis, S. Durbha

引用次数: 6

Flood Depth Estimation from Web Images 从Web图像估计洪水深度

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365542

Zonglin Meng, Bo Peng, Qunying Huang

{"title":"Flood Depth Estimation from Web Images","authors":"Zonglin Meng, Bo Peng, Qunying Huang","doi":"10.1145/3356395.3365542","DOIUrl":"https://doi.org/10.1145/3356395.3365542","url":null,"abstract":"Natural hazards have been resulting in severe damage to our cities, and flooding is one of the most disastrous in the U.S and worldwide. Therefore, it is critical to develop efficient methods for risk and damage assessments after natural hazards, such as flood depth estimation. Existing works primarily leverage photos and images capturing flood scenes to estimate flood depth using traditional computer vision and machine learning techniques. However, the advancement of deep learning (DL) methods make it possible to estimate flood depth more accurate. Therefore, based on state-of-the-art DL technique (i.e., Mask R-CNN) and publicly available images from the Internet, this study aims to investigate and improve the flood depth estimation. Specifically, human objects are detected and segmented from flooded images to infer the floodwater depth. This study provides a new framework to extract critical information from large accessible online data for rescue teams or even robots to carry out appropriate plans for disaster relief and rescue missions in the urban area, shedding lights on the real-time detection of the flood depth.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126288632","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: 9

Mobility Pattern Analysis for Power Restoration Activities Using Geo-Tagged Tweets 使用地理标记推文的电力恢复活动的移动模式分析

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365547

B. Kar, Jacob Ethridge

{"title":"Mobility Pattern Analysis for Power Restoration Activities Using Geo-Tagged Tweets","authors":"B. Kar, Jacob Ethridge","doi":"10.1145/3356395.3365547","DOIUrl":"https://doi.org/10.1145/3356395.3365547","url":null,"abstract":"In this study, we analyzed mobility patterns of at-risk populations affected by an extreme event using geotagged tweets to geo-target power restoration efforts. Unlike other studies that have used tweets to facilitate emergency management activities, we used 1.5 million geotagged tweets generated during Hurricane Sandy (2012) to determine the mobility patterns and geospatial distribution of impacted populations who experienced power outage before, during and after the hurricane. We implemented a three-step analytical framework to: (i) analyze tweet contents with visual methods, including dendrograms, word clouds to identify common keywords pertaining to power outage; (ii) identify target users whose tweets contained information about power outages; and (iii) create a user-tweet locations matrix and an origin-destination matrix to examine clusters of target users and their mobility patterns. Preliminary results indicate that potential clusters were present in and around New York city, Philadelphia, Washington D.C. and Baltimore, which were used as potential evacuation destination cities after hurricane Sandy. The travel pattern and destination information can be used to (i) mobilize restoration efforts by utility companies and (ii) address resource allocation needs both in impacted and destination cities. Future work will focus on analyzing potential destinations for different origins and travel-time to identify evacuation routing patterns.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"38 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120848586","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

Using Digital Trace Data to Identify Regions and Cities 使用数字跟踪数据识别区域和城市

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365539

Christa M. Brelsford, Gautam Thakur, Rudy Arthur, Hywel T. P. Williams

{"title":"Using Digital Trace Data to Identify Regions and Cities","authors":"Christa M. Brelsford, Gautam Thakur, Rudy Arthur, Hywel T. P. Williams","doi":"10.1145/3356395.3365539","DOIUrl":"https://doi.org/10.1145/3356395.3365539","url":null,"abstract":"A greater understanding of human dynamics as they play out in both physical space and through interpersonal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight into the space-time distribution of population and their activity patterns, while interpersonal communication can now be measured at the population scale through digital interactions. In this work, we propose a novel method to discover these dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We compare these results to US census regions and economic and infrastructural networks. We find that the broad spatial delineation of communities and sub-communities is consistent with United States regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of social regions that can be consistently applied anywhere there is a sufficiently rich data source. A deeper understanding of urban social structure measured by spatially embedded communication networks can enable a better understanding of the interactions between urban social and physical contexts. This, in turn, may enable urban managers and policy makers to identify strategies for supporting urban resilience.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121313057","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: 2

Structuralizing Disaster-scene Data through Auto-captioning 通过自动标注来结构化灾难场景数据

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365671

Alina Klerings, Shimin Tang, Zhiqiang Chen

引用次数: 0

Towards an Integrated and Realtime Wayfinding Framework for Flood Events 面向洪水事件的集成实时寻路框架

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365543

Jerry Mount, Yazeed Alabbad, I. Demir

{"title":"Towards an Integrated and Realtime Wayfinding Framework for Flood Events","authors":"Jerry Mount, Yazeed Alabbad, I. Demir","doi":"10.1145/3356395.3365543","DOIUrl":"https://doi.org/10.1145/3356395.3365543","url":null,"abstract":"City planners can encounter severe challenges during natural disasters. Flooding, for example, is considered as the number one cause of infrastructure damage across the world. Flooding can have a significant impact on personal property, commercial assets, and essential infrastructure, including power and gas delivery, and transportation. This paper focuses on the effects of flooding on transportation systems in the State of Iowa under a variety of flood scenarios, including 50, 100, 200, and 500-year return probabilities. We explore the impacts of flooding from institutional support (e.g., services including police, fire, and EMS) and general population (i.e., individuals distributed across cities) perspectives. Graph-theoretic methods are used in this study to determine the effects of flooding on road networks due to potential removal of paths that were once viable. This paper presents preliminary research into flood impacts on road infrastructure in Iowa and the development of an integrated real-time framework for analyzing those impacts. In future work, we plan to extend the framework developed in this study to provide a generalized decision-support system for cities and individuals. The framework will be open so city planners will be able explore \"what if\" flooding scenarios to find vulnerable areas and populations in their jurisdiction. These areas can be made more resilient to flooding effects by increasing the elevation of important roads, changing flow patterns, or increasing the height of bridges.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115181920","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: 4

Topic Modeling To Contextualize Event-Based Datasets: The Colombian Peace Process 主题建模上下文化基于事件的数据集:哥伦比亚和平进程

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365540

A. Daughton, Geoffrey Fairchild, C. W. Ross, S. D. Valle

引用次数: 1

Vision for a Holistic Smart City-HSC: Integrating Resiliency Framework via Crowdsourced Community Resiliency Information System (CRIS) 整体智慧城市愿景:通过众包社区弹性信息系统(CRIS)整合弹性框架

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities Pub Date : 2019-11-05 DOI: 10.1145/3356395.3365541

B. Dixon, Rebecca A. Johns

{"title":"Vision for a Holistic Smart City-HSC: Integrating Resiliency Framework via Crowdsourced Community Resiliency Information System (CRIS)","authors":"B. Dixon, Rebecca A. Johns","doi":"10.1145/3356395.3365541","DOIUrl":"https://doi.org/10.1145/3356395.3365541","url":null,"abstract":"This vision paper discusses future directions and existing gaps in integrating smart city initiatives with resilience frameworks. It proposes the use of a multi-modular crowdsourced Community Resiliency Information System (CRIS) to overcome traditional smart citiesâĂŹ focus on infrastructure and grid vulnerabilities/resiliency while overlooking socio-economic vulnerabilities. CRIS is conceptualized based on our previous research which identified the importance of customized information and targeted resources to foster preparedness, adaptation and resiliency among diverse communities. Our proposed vision of a smart city integrated with CRIS allows scalable and customizable solutions for policymakers using information generated âĂŸby the peopleâĂŹ, thus ensuring participation of diverse communities in smart city technology, thus creating a Holistic Smart City (HSC). CRIS will foster a two-way communication between government and communities by creating a grassroots, community-based, technology-enhanced needs assessment and disaster-response information system. Among other benefits, CRIS will generate and organize data to be used by the local community and policy makers and foster ongoing dialogue between neighborhoods and policymakers. CRIS will foster social capital at the neighborhood level by increasing grassroots knowledge and access to resources and information, fostering preparedness, adaptation, and resiliency/recovery, and aiding decision-makers in resource allocation and customized communications. CRIS moves the smart city beyond a mere infrastructure to create an interactive space for information exchange, democratic participation and a collaborative resilience-building process.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126459802","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: 3