UrbanGIS '16最新文献

Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities 隐藏在城市中的风格:对十个主要城市的地理定位airbnb租赁图像的分析

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007547

S. Rahimi, Xi Liu, Clio Andris

{"title":"Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities","authors":"S. Rahimi, Xi Liu, Clio Andris","doi":"10.1145/3007540.3007547","DOIUrl":"https://doi.org/10.1145/3007540.3007547","url":null,"abstract":"In this article, we analyze geolocated Airbnb rental images in ten major cities. Airbnb is a hallmark institution in the sharing economy, allowing anyone with a bed and shelter to act like a micro-hotel, i.e. a bed-and-breakfast for other travelers. Travelers often spend less on Airbnb rentals than hotels and get a residential experience in a new place.\u0000 Since hosts advertise their rentals on Airbnb, the site has a wealth of residential interior images from all over the world: from rural Africa to downtown Manhattan. As part of an ongoing project, we have downloaded over 200,000 images posted on Airbnb to ask: how do people decorate their homes in different locales? Do they use certain colors, or have a certain ornate or simple style?\u0000 Here, we test ten major metropolitan areas using image rating responses from Mechanical Turk as well as automated image color predominance routines to investigate geographical differences in interior styles. We find overarching indicators of globalization and a lack of local culture in the case of color, but that different neighborhoods within cities have different levels or ornateness when decorating their properties. The results of this research can also help to identify the kinds of interiors that are more pleasant in the eyes of customers.","PeriodicalId":136234,"journal":{"name":"UrbanGIS '16","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116820388","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: 18

Data-driven benchmarking of building energy performance at the city scale 数据驱动的城市建筑能源绩效基准

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007541

Zheng Yang, J. Roth, Rishee K. Jain

{"title":"Data-driven benchmarking of building energy performance at the city scale","authors":"Zheng Yang, J. Roth, Rishee K. Jain","doi":"10.1145/3007540.3007541","DOIUrl":"https://doi.org/10.1145/3007540.3007541","url":null,"abstract":"Cities across the country (20 to date) are rapidly passing laws to mandate the collection and disclosure of energy usage data with the hopes that such data could be utilized to benchmark building energy performance and provide a basis for designing and deploying efficiency measures. However, numerous municipalities are struggling to translate such data into actionable insights on which buildings are the best candidates for energy efficiency interventions. Current studies are limited in their ability to compare energy efficiency of buildings at the city scale and maintain interpretability necessary to result in effective decision making that can facilitate municipal policy and program design. In this paper, we present an integrated data-driven method based on recursive partitioning and stochastic frontier analysis to benchmark energy performance of building stock across an entire city. We implement the proposed method on a dataset of over 10,000 buildings in New York City. The preliminary results begin to quantify the potential for building energy efficiency and give further support to city officials for examining effects of possible interventions. We aim to establish a robust methodology that provides a standard way to benchmark building energy performance at the city scale, which can be easily applied to other cities across the country.","PeriodicalId":136234,"journal":{"name":"UrbanGIS '16","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129492694","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

A spectral analysis of crimes in San Francisco 旧金山犯罪的光谱分析

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007544

Luca Venturini, Elena Baralis

引用次数: 12

Ontology-based urban data exploration 基于本体的城市数据探索

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007550

B. Balasubramani, Vivek R. Shivaprabhu, Smitha Krishnamurthy, I. Cruz, T. Malik

引用次数: 11

Trajbase: searching trajectories in multi-region Trajbase:搜索多区域轨迹

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007543

Renjie Zheng, Qin Liu, Hanjun Mao, Hongming Zhu, Weixiong Rao

引用次数: 0

A feature-based method for traffic anomaly detection 基于特征的交通异常检测方法

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007545

Youcheng Wang, Jian Xu, Ming Xu, Ning Zheng, Jingsheng Jiang, Kaiwei Kong

{"title":"A feature-based method for traffic anomaly detection","authors":"Youcheng Wang, Jian Xu, Ming Xu, Ning Zheng, Jingsheng Jiang, Kaiwei Kong","doi":"10.1145/3007540.3007545","DOIUrl":"https://doi.org/10.1145/3007540.3007545","url":null,"abstract":"The wide spread use of GPS-enabled devices facilitates us to sense the movement of vehicles. Detecting anomalous movement behavior on road segments can benefit both drivers and transportation authorities. An important challenge behind this is how to detect these anomalies effectively and timely under large scale of raw GPS trajectories. In this light, we propose a Feature-Based method for Traffic Anomaly Detection (FBTAD). A key observation is that road segments, where these incidents turns up, tend to have their vehicle flow features changed in a short period of time. For example, a traffic accident may immediately and significantly slow down the travel speed on a road segment. In this paper, we first map-match raw trajectories. Then we calculate the road segments' traffic features in each time slot, e.g., 10 minutes, and introduce an offline spatial-temporal index for speeding up the anomaly detection process. Finally, we retrieve anomaly candidates by checking the road segment's traffic flow acceleration based on the index built above, and examine candidates' density change ratio or moving objects' outflow ratio to further infer traffic anomalies. The effectiveness and efficiency of our approach is validated by extensive experimentation. Our evaluations show that the method proposed in this paper is able to detect traffic anomalies more efficiently as well as earlier than the baseline method.","PeriodicalId":136234,"journal":{"name":"UrbanGIS '16","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124019701","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: 13

Analysis of smart card data for understanding spatial changes in consumption-oriented human flows 分析智能卡数据，了解以消费为导向的人流的空间变化

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007542

Takashi Nicholas Maeda, Junichiro Mori, F. Toriumi, H. Ohashi

{"title":"Analysis of smart card data for understanding spatial changes in consumption-oriented human flows","authors":"Takashi Nicholas Maeda, Junichiro Mori, F. Toriumi, H. Ohashi","doi":"10.1145/3007540.3007542","DOIUrl":"https://doi.org/10.1145/3007540.3007542","url":null,"abstract":"This paper studies how commercial facilities and events change the spatial structures of consumption-oriented human flows. We utilize the smart card data of public transportation in the Kansai Area, Japan. In this paper, we develop a method to understand the main purpose of daily human flow from a station to another station by classifying the time series distributions of the number of passengers. We distinguish consumption-oriented human flows from human flows with other purposes, by checking the shapes of time-series distributions of passengers and examples of destinations. For example, the main purposes of human flows whose destination stations are near sightseeing resorts or amusement parks can be regarded as consumption. Then we extract clusters of departure stations sharing the same destinations in consumption-oriented human flows. Finally, we utilize the results for understanding how openings and expansions of commercial facilities affect the spatial clusters of consumers' mobility and what kind of stations are likely to be the origins of human flows to events such as art exhibitions and sport competitions by using some case studies.","PeriodicalId":136234,"journal":{"name":"UrbanGIS '16","volume":"368 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132264876","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

Exploiting mobile phone data for multi-category land use classification in Africa 利用移动电话数据进行非洲多类别土地利用分类

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007549

Huina Mao, Gautam Thakur, B. Bhaduri

引用次数: 7

Route planning for locations based on trajectory segments 基于轨迹段的位置路线规划

UrbanGIS '16 Pub Date : 2016-10-31 DOI: 10.1145/3007540.3007546

Jingsheng Jiang, Chong Xu, Jian Xu, Ming Xu, Ning Zheng, Kaiwei Kong

{"title":"Route planning for locations based on trajectory segments","authors":"Jingsheng Jiang, Chong Xu, Jian Xu, Ming Xu, Ning Zheng, Kaiwei Kong","doi":"10.1145/3007540.3007546","DOIUrl":"https://doi.org/10.1145/3007540.3007546","url":null,"abstract":"Route planning for a set of locations based on trajectory searching is a hot topic. To obtain previous drivers' knowledge on route selection, some existing works search trajectories which are spatially close to the query locations. However, these trajectories may be only close to partial query locations or go to other locations beyond the query location set, which lead the algorithm to a poor performance. In this paper, we study a new model called Route Planning for Locations Based on Trajectory Segment (RPBTS). Given a set of ordered query locations, in order to pass as close as to the query locations, we plan a route by combining some intersecting trajectory segments. A greedy algorithm is employed to retrieve the optimal combinations which doesn't contain the two undesirable conditions mentioned above. To enhance the performance of the algorithm, we construct a regional landmark graph, where a regional landmark is a road segment frequently traversed by drivers in a certain region. Based on this graph, it's very likely a query location can be converted to a regional landmark. Finally, we propose a Random Selection strategy to further improve the efficiency. The effectiveness of our method is verified by empirical study based on a real trajectory data set.","PeriodicalId":136234,"journal":{"name":"UrbanGIS '16","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123500544","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

Combining maps and street level images for building height and facade estimation 结合地图和街道水平图像的建筑高度和立面估计

UrbanGIS '16 Pub Date : 2016-01-28 DOI: 10.1145/3007540.3007548

Jiangye Yuan, A. Cheriyadat

引用次数: 16