Avipsa Roy, Edouard Fouché, Rafael Rodriguez Morales, Gregor Möhler
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.
{"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}
Chrysovalantis Anastasiou, Jianfa Lin, Chao He, Yao-Yi Chiang, Cyrus Shahabi
This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.
{"title":"ADMSv2","authors":"Chrysovalantis Anastasiou, Jianfa Lin, Chao He, Yao-Yi Chiang, Cyrus Shahabi","doi":"10.1145/3356395.3365544","DOIUrl":"https://doi.org/10.1145/3356395.3365544","url":null,"abstract":"This paper presents ADMSv2, an end-to-end data-driven system that enables real-time and historical data analytics and machine learning tasks over big, streaming, spatiotemporal data. ADMSv2 employs a unified multi-layered architecture that integrates several open-source frameworks to collect, store, manage, and analyze a variety of data sources, including massive traffic sensor data, bus trajectory data, transportation network data, and traffic incidents data. ADMSv2 enables numerous applications in intelligent transportation, urban planning, public policy, and emergency response, all of which are critical for city resilience. Here, we demonstrate three application scenarios running on top of ADMSv2 to showcase the efficiency of its capabilities of query processing on real-world streaming and historical data as well as real-time data analysis using deep learning for traffic forecasting.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"30 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":"122096408","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}
Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatiotemporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.
{"title":"Semantics-enabled Spatio-Temporal Modeling of Earth Observation Data: An application to Flood Monitoring","authors":"Kuldeep R. Kurte, Abhishek V. Potnis, S. Durbha","doi":"10.1145/3356395.3365545","DOIUrl":"https://doi.org/10.1145/3356395.3365545","url":null,"abstract":"Extreme events such as urban floods are dynamic in nature, i.e. they evolve with time. The spatiotemporal analysis of such disastrous events is important for understanding the resiliency of an urban system during these events. Remote Sensing (RS) data is one of the crucial earth observation (EO) data sources that can facilitate such spatiotemporal analysis due to its wide spatial coverage and high temporal availability. In this paper, we propose a discrete mereotopology (DM) based approach to enable representation and querying of spatiotemporal information from a series of multitemporal RS images that are acquired during a flood disaster event. We represent this spatiotemporal information using a semantic model called Dynamic Flood Ontology (DFO). To establish the effectiveness and applicability of the proposed approach, spatiotemporal queries relevant during an urban flood scenario such as, show me road segments that were partially flooded during the time interval t1 have been demonstrated with promising results.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"174 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":"133319396","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}
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.
{"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}
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.
{"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}
Christa M. Brelsford, Gautam Thakur, Rudy Arthur, Hywel T. P. Williams
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.
{"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}
Disaster-scene images documenting the magnitude and effects of natural disasters nowadays can be easily collected through crowdsourcing aided by mobile technologies (e.g., smartphones or drones). One challenging issue that confronts the first-responders who desire the use of such data is the non-structured nature of these crowdsourced images. Among other techniques, one natural way is to structuralize disaster-scene images through captioning. Through captioning, their imagery contents are augmented by descriptive captions that further enable more effective search and query (S&Q). This work presents a preliminary test by exploiting an end-to-end deep learning framework with a linked CNN-LSTM architecture. Demonstration of the results and quantitative evaluation are presented that showcase the validity of the proposed concept.
{"title":"Structuralizing Disaster-scene Data through Auto-captioning","authors":"Alina Klerings, Shimin Tang, Zhiqiang Chen","doi":"10.1145/3356395.3365671","DOIUrl":"https://doi.org/10.1145/3356395.3365671","url":null,"abstract":"Disaster-scene images documenting the magnitude and effects of natural disasters nowadays can be easily collected through crowdsourcing aided by mobile technologies (e.g., smartphones or drones). One challenging issue that confronts the first-responders who desire the use of such data is the non-structured nature of these crowdsourced images. Among other techniques, one natural way is to structuralize disaster-scene images through captioning. Through captioning, their imagery contents are augmented by descriptive captions that further enable more effective search and query (S&Q). This work presents a preliminary test by exploiting an end-to-end deep learning framework with a linked CNN-LSTM architecture. Demonstration of the results and quantitative evaluation are presented that showcase the validity of the proposed concept.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"138 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":"132402316","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}
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.
{"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}
A. Daughton, Geoffrey Fairchild, C. W. Ross, S. D. Valle
Colombia suffered civil conflict for over five decades resulting in thousands of deaths and kidnappings and millions of displaced citizens. A peace process between the government and the Revolutionary Armed Forces of Colombia (FARC) was negotiated in 2016. Quantifying public sentiment during the process may help us understand the role of social media in shaping opinions and influencing decision makers. Obtaining these viewpoints using traditional survey approaches is costly and logistically challenging. Instead, we used Twitter and news data between 2010-2018 to analyze trends before, during, and after the settlement. We used unsupervised learning methods to identify topics and measure their sentiment over time; we then compare those results to events in the Integrated Crisis Early Warning System (ICEWS) dataset.
{"title":"Topic Modeling To Contextualize Event-Based Datasets: The Colombian Peace Process","authors":"A. Daughton, Geoffrey Fairchild, C. W. Ross, S. D. Valle","doi":"10.1145/3356395.3365540","DOIUrl":"https://doi.org/10.1145/3356395.3365540","url":null,"abstract":"Colombia suffered civil conflict for over five decades resulting in thousands of deaths and kidnappings and millions of displaced citizens. A peace process between the government and the Revolutionary Armed Forces of Colombia (FARC) was negotiated in 2016. Quantifying public sentiment during the process may help us understand the role of social media in shaping opinions and influencing decision makers. Obtaining these viewpoints using traditional survey approaches is costly and logistically challenging. Instead, we used Twitter and news data between 2010-2018 to analyze trends before, during, and after the settlement. We used unsupervised learning methods to identify topics and measure their sentiment over time; we then compare those results to events in the Integrated Crisis Early Warning System (ICEWS) dataset.","PeriodicalId":232191,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances on Resilient and Intelligent Cities","volume":"60 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":"117178202","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}
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.
{"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}
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