Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics最新文献

Extraction of Road Maintenance Criteria using Machine Learning and Spatial Information 基于机器学习和空间信息的道路养护标准提取

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152187

Hiroya Maeda, Y. Sekimoto, Toshikazu Seto, Takehiro Kashiyama, Hiroshi Omata

{"title":"Extraction of Road Maintenance Criteria using Machine Learning and Spatial Information","authors":"Hiroya Maeda, Y. Sekimoto, Toshikazu Seto, Takehiro Kashiyama, Hiroshi Omata","doi":"10.1145/3152178.3152187","DOIUrl":"https://doi.org/10.1145/3152178.3152187","url":null,"abstract":"Infrastructure maintenance requires extensive financial and human resources, and a lack of these resources---and, in particular, a shortage of experts---is a problem in many countries and regions around the world. In response to such circumstances, there is considerable research on infrastructure damage-detection methods using camera images and machine-learning. However, even if a large number of damaged parts are found using such methods, the decision whether to repair damaged areas is nevertheless determined empirically, by taking into account several factors such as road statistics and the regional characteristics. For these reasons, the current situation is that municipalities that lack experts cannot make comprehensive decisions regarding repairs. Therefore, in this research, we extracted maintenance management standards and automated decision-making using the decisions made by local government officials regarding damaged roads in Japan. We focused on roads, because roads are considered to be one of the most influential infrastructure. In order to do so, we cooperated with six municipalities in Japan. We combined statistical information regarding damaged roads with regional characteristics. As a result, in a very understandable way, we were then able to reproduce the decisions made by experts with an accuracy of 0.75. Our research has the potential to enable automated decision-making in the future.","PeriodicalId":378940,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116193976","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

Ontology-based Instance Matching for Geospatial Urban Data Integration 基于本体的城市地理空间数据集成实例匹配

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152186

Vivek R. Shivaprabhu, B. Balasubramani, I. Cruz

{"title":"Ontology-based Instance Matching for Geospatial Urban Data Integration","authors":"Vivek R. Shivaprabhu, B. Balasubramani, I. Cruz","doi":"10.1145/3152178.3152186","DOIUrl":"https://doi.org/10.1145/3152178.3152186","url":null,"abstract":"To run a smart city, data is collected from disparate sources such as IoT devices, social media, private and public organizations, and government agencies. In the US, the City of Chicago has been a pioneer in the collection of data and in the development of a framework, called OpenGrid, to curate and analyze the collected data. OpenGrid is a geospatial situational awareness platform that allows policy makers, service providers, and the general public to explore city data and to perform advanced data analytics to enable planning of services, prediction of events and patterns, and identification of incidents across the city. This paper presents the instance matching module of GIVA, a Geospatial data Integration, Visualization, and Analytics platform, as applied to the integration of information related to businesses, which is spread across several datasets. In particular, we describe the integration of two datasets, Business Licenses and Food Inspections, so as to enable predictive analytics to determine which food establishments the city should inspect first. The paper describes semantic web-based instance matching mechanisms to compare the Business Names and Address fields.","PeriodicalId":378940,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129698951","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: 7

flowHDBSCAN: A Hierarchical and Density-Based Spatial Flow Clustering Method 一种基于层次和密度的空间流聚类方法

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152189

Ran Tao, J. Thill, C. Depken, M. Kashiha

{"title":"flowHDBSCAN: A Hierarchical and Density-Based Spatial Flow Clustering Method","authors":"Ran Tao, J. Thill, C. Depken, M. Kashiha","doi":"10.1145/3152178.3152189","DOIUrl":"https://doi.org/10.1145/3152178.3152189","url":null,"abstract":"Understanding the patterns and dynamics of spatial origin-destination flow data has been a long-standing goal of spatial scientists. This study aims at developing a new flow clustering method called flowHDBSCAN, which has the potential to be applied to various urban dynamics issues such as spatial movement analysis and intelligent transportation systems. Flows entail origin and destinations pairs, at the exclusion of the actual path in-between. The method combines density-based clustering and hierarchical clustering approaches and extends them to the context of spatial flows. Not only can it extract flow clusters from various situations including varying flow densities, lengths, directions, and hierarchies, but it also provides an effective way to reveal the potentially hierarchical data structure of the clusters. Common issues such as the modifiable areal unit problem (MAUP) of flow endpoints, false positive errors on short flows, and loss of spatial information are well handled. Moreover, the sole-parameter design guarantees its ease of use and practicality. Experiments are conducted with both a synthetic dataset and an eBay online trade flow dataset in the contiguous U.S.","PeriodicalId":378940,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116898331","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: 10

Trajectory Query Based on Trajectory Segments with Activities 基于带有活动的轨迹段的轨迹查询

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152180

Kaiwei Kong, Jian Xu, Ming Xu, Liming Tu, Y. Wu, Zhi Chen

引用次数: 2

Automatic Spatio-temporal Indexing to Integrate and Analyze the Data of an Organization 自动时空索引整合和分析组织数据

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152185

Craig A. Knoblock, Aparna R. Joshi, Abhishek Megotia, Minh Pham, Chelsea Ursaner

引用次数: 5

QUIET ZONE: Reducing The Communication Cost of Continuous Spatial Queries 安静区:降低连续空间查询的通信成本

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152179

A. Hidayat, M. A. Cheema

引用次数: 0

Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing 通过聚集和生长，不断将建筑推广到建成区

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152188

Dongliang Peng, G. Touya

引用次数: 7

Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use 土地利用精细制图开源数据的定量比较

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152182

Xueqing Deng, S. Newsam

引用次数: 13

Linked Data and Visualization: Two Sides of the Transparency Coin 关联数据和可视化:透明硬币的两面

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152191

Auriol Degbelo

引用次数: 8

Trajectory Annotation by Discovering Driving Patterns 发现驾驶模式的轨迹注释

Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics Pub Date : 2017-11-07 DOI: 10.1145/3152178.3152184

Sobhan Moosavi, Behrooz Omidvar-Tehrani, R. Ramnath

{"title":"Trajectory Annotation by Discovering Driving Patterns","authors":"Sobhan Moosavi, Behrooz Omidvar-Tehrani, R. Ramnath","doi":"10.1145/3152178.3152184","DOIUrl":"https://doi.org/10.1145/3152178.3152184","url":null,"abstract":"The ubiquity and variety of available sensors has enabled the collection of voluminous datasets of car trajectories that enable analysts to make sense of driving patterns and behaviors. One approach to obtain driving behaviors is to break a trajectory into its underlying patterns and then analyze these patterns (aka segmentation). To validate and improve automated trajectory segmentation algorithms, there is a crucial need for a ground-truth against which to compare the results of the algorithms. To the best of our knowledge, no such publicly available ground-truth of car trajectory annotations exists. In this paper, we introduce a trajectory annotation framework and use it to annotate a real-world dataset of personal car trajectories. Our annotation methodology consists of a crowd-sourcing step followed by a precise process of expert aggregation. Our annotation identifies segment borders, and then labels the segment with its type (e.g. speed-up, turn, merge, etc.). The output of our project is a dataset of annotated car trajectories (DACT), and is publicly available for use by the spatiotemporal research community at https://goo.gl/XgsxyJ.","PeriodicalId":378940,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122114509","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: 10