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1.
Social media applications are widely deployed in mobile platforms equipped with built-in GPS tracking devices, and these devices have led to an unprecedented collection of geolocated data (geo-tags). Geo-tags, along with place names, offer new opportunities to explore the trajectory and mobility patterns of social media users. However, trajectory data captured by social media are sparsely and irregularly spaced and therefore have varying degrees of resolution in both space and time. Previous studies on next location prediction are mostly applicable for detecting the upcoming location of a moving object using dense GPS trajectories where locations are recorded at regular time intervals (e.g., 1 minute). Additionally, point features are commonly used to represent the locations of visits, but using point features cannot capture the variability of human mobility. This article introduces a new methodology to predict an individual’s next location based on sparse footprints accumulated over a long time period using social networks, and uses polygons to represent the location corresponding to the physical activity area of individuals. First, the density-based spatial clustering algorithm is employed to discover the most representative activity zones that an individual frequently visits on a daily basis, and a polygon-based region is then derived for each representative activity zone. A sparse mobility Markov chain model considering both the movements and online behaviors of the social media user is trained and used to predict the user’s next location. Initial experiments with a group of Washington DC Twitter users demonstrate that the proposed methodology successfully discovers the activity regions and predicts the user’s next location with accuracy approaching 78.94%.  相似文献   

2.
The proliferation of digital cameras and the growing practice of online photo sharing using social media sites such as Flickr have resulted in huge volumes of geotagged photos available on the Web. Based on users' traveling preferences elicited from their travel experiences exposed on social media sites by sharing geotagged photos, we propose a new method for recommending tourist locations that are relevant to users (i.e., personalization) in the given context (i.e., context awareness). We obtain user-specific travel preferences from his/her travel history in one city and use these to recommend tourist locations in another city. Our technique is illustrated on a sample of publicly available Flickr dataset containing photos taken in various cities of China. Results show that our context-aware personalized method is able to predict tourists' preferences in a new or unknown city more precisely and generate better recommendations compared to other state-of-the-art landmark recommendation methods.  相似文献   

3.
ABSTRACT

Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users’ home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.  相似文献   

4.
ABSTRACT

Social networks have played a crucial role as information channels for people to understanding their daily lives beyond merely being communication tools. In particular, coupling social networks with geographic location has boosted the worth of social media to not only enable comprehension of the effects of natural phenomena such as global warming and disasters, but also the social patterns of human societies. However, the high rate of social data generation and the large amounts of noisy data makes it difficult to directly apply social media to decision-making processes. This article proposes a new system of analyzing the spatio-temporal patterns of social phenomena in real time and the discovery of local topics based on their latent spatio-temporal relationships. We will first describe a model that represents the local patterns of populations of geo-tagged social media. We will then define a local topic whose keywords share a region in space and time and present a system implementation based on existing open source technologies. We evaluated the model of local topics with several ways of visualization in experiments and demonstrated a certain social pattern from a dataset of daily Twitter streams. The results obtained from experiments revealed certain keywords had a strong spatio-temporal proximity even though they did not occur in the same message.  相似文献   

5.
ABSTRACT

Datasets collecting the ever-changing position of moving individuals are usually big and possess high spatial and temporal resolution to reveal activity patterns of individuals in greater detail. Information about human mobility, such as ‘when, where and why people travel’, is contained in these datasets and is necessary for urban planning and public policy making. Nevertheless, how to segregate the users into groups with different movement and behaviours and generalise the patterns of groups are still challenging. To address this, this article develops a theoretical framework for uncovering space-time activity patterns from individual’s movement trajectory data and segregating users into subgroups according to these patterns. In this framework, individuals’ activities are modelled as their visits to spatio-temporal region of interests (ST-ROIs) by incorporating both the time and places the activities take place. An individual’s behaviour is defined as his/her profile of time allocation on the ST-ROIs she/he visited. A hierarchical approach is adopted to segregate individuals into subgroups based upon the similarity of these individuals’ profiles. The proposed framework is tested in the analysis of the behaviours of London foot patrol police officers based on their GPS trajectories provided by the Metropolitan Police.  相似文献   

6.
Abstract

Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation.

In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the ‘snapshot’ model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.  相似文献   

7.
Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our method trains two types of base classifiers to jointly predict the next travel destination: (1) The K-nearest neighbor (KNN) classifier quantifies users’ location history, weather condition, temperature and seasonality and uses a feature-weighted distance model to predict a user’s personalized interests in an unvisited location. (2) A Bayes classifier introduces a smooth kernel function to estimate a-priori probabilities of features and then combines these probabilities to predict a user’s latent interests in a location. All the outcomes from these subclassifiers are merged into one final prediction result by using the Borda count voting method. We evaluated our method on geo-tagged Flickr photos and Beijing weather data collected from 1 January 2005 to 1 July 2016. The results demonstrated that our ensemble approach outperformed 12 other baseline models. In addition, the results showed that our framework has better prediction accuracy than do context-aware significant travel-sequence-patterns recommendations and frequent travel-sequence patterns.  相似文献   

8.
ABSTRACT

Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays.  相似文献   

9.
Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R2) derived from the data with and without implementing the activity change detection. The results show that the R2 is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.  相似文献   

10.
Attractive regions can be detected and recommended by investigating users’ online footprints. However, social media data suffers from short noisy text and lack of a-priori knowledge, impeding the usefulness of traditional semantic modelling methods. Another challenge is the need for an effective strategy for the selection/recommendation of candidate regions. To address these challenges, we propose a comprehensive workflow which combines semantic and location information of social media data to recommend thematic urban regions to users with specific interests. This workflow is novel in: (1) developing a data-driven geographic topic modelling method which utilizes the co-occurrence patterns of self-explanatory semantic information to detect semantic communities; (2) proposing a new recommendation strategy with the consideration of region’s spatial scale. The workflow was implemented using a real-world dataset and evaluation conducted at three different levels: semantic representativeness, topic identification and recommendation desirability. The evaluation showed that the semantic communities detected were internally consistent and externally differentiable and that the recommended regions had a high degree of desirability. The work has demonstrated the effectiveness of self-explanatory semantic information for geographic topic modelling and highlighted the importance of including region spatial scale into the model for an effective region recommending strategy.  相似文献   

11.
ObjectivesUsing publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York.MethodsWe utilize 2.8 million tweets collected between February–August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics.ResultsFor a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths.ConclusionsSocial media can be leveraged to provide greater understanding of the well-being and health behaviors of communities—information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.  相似文献   

12.
Background and purposeTerrorism is a real and present danger. The build-up to an attack includes planning, travel, and reconnaissance which necessarily require the offender to move through their environment. Whilst research has examined patterns of terrorist attack locations, with a few exceptions (e.g. Rossmo & Harries, 2011), it has not examined the spatial behavior of the terrorists themselves. In this paper, we investigate whether the spatial mobility patterns of terrorists resemble those of criminals (and the wider population) and if these change in the run up to their attacks.MethodUsing mobile phone data records for the ringleaders of four different UK-based terrorist plots in the months leading up to their attacks, we examine the frequency with which terrorists visit different locations, how far they travel from key anchor points such as their home, the distance between sequential cell-site hits and how their range of movement varies as the planned time to attack approaches.ConclusionsLike the wider population (and criminals), the sample of terrorists examined exhibited predictable patterns of spatial behavior. Most movements were close to their home location or safe house, and they visited a relatively small number of locations most of the time. Disaggregating these patterns over time provided mixed evidence regarding the way in which their spatial activity changed as the time to the planned attack approached. The findings are interpreted in terms of how they inform criminological understanding of the spatial behavior of terrorists, and the implications for law enforcement.  相似文献   

13.
Modeling the geographic distribution of tourists at a tourist destination is crucial when it comes to enhancing the destination’s resilience to disasters and crises, as it enables the efficient allocation of limited resources to precise geographic locations. Seldom have existing studies explored the geographic distribution of tourists through understanding the mechanisms behind it. This article proposes to couple maximum entropy modeling with geotagged social media data to determine the geographic distribution of tourists in order to facilitate disaster and crisis management at tourist destinations. As one of the most popular tourist destinations in the United States, San Diego was chosen as the study area to demonstrate the proposed approach. We modeled the tourist geographic distribution in the study area by quantifying the relationship between the distribution and five environmental factors, including land use, land parcel, elevation, distance to the nearest major road and distance to the nearest transit stop. The geographic distribution’s dependency on and sensitivity to the environmental factors were uncovered. The model was subsequently applied to estimate the potential impacts of one simulated tsunami disaster and one simulated traffic breakdown due to crisis events such as a political protest or a fire hazard. As such, the effectiveness of the approach has been demonstrated with specific disaster and crisis scenarios.  相似文献   

14.
ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

15.
A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of this study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations . We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied\ to a variety of urban applications.  相似文献   

16.
基于社交媒体签到数据的城市居民暴雨洪涝响应时空分析   总被引：3，自引：2，他引：1  
王波  甄峰  孙鸿鹄 《地理科学》2020,40(9):1543-1552
暴雨洪涝等小型地域性气候灾害给城市韧性带来挑战。以南京暴雨洪涝为例,通过挖掘新浪微博签到数据,构建公众感知指数和公众情绪指数,分析居民对暴雨洪涝响应的时空格局。在时间维度上,居民对暴雨洪涝的响应主要集中在暴雨洪涝期,并随灾害的严重程度而变化;在暴雨洪涝期内,居民在社交媒体上对暴雨洪涝的响应集中在早、晚高峰。在空间维度上,居民对暴雨洪涝的响应集中在主城区和3个新市区;重要交通基础枢纽地区和低海拔、经历快速城市化的新市区的居民对暴雨洪涝担忧程度更高。时空分析表明,暴雨洪涝对居民的交通出行影响最明显。基于时空间分析,最后从硬件和软件设施上为提升暴雨洪涝的城市韧性提供相关政策建议。  相似文献   

17.
Tracking spatial and temporal trends of events (e.g. disease outbreaks and natural disasters) is important for situation awareness and timely response. Social media, with increasing popularity, provide an effective way to collect event-related data from massive populations and thus a significant opportunity to dynamically monitor events as they emerge and evolve. While existing research has demonstrated the value of social media as sensors in event detection, estimating potential time spans and influenced areas of an event from social media remains challenging. Challenges include the unstable volumes of available data, the spatial heterogeneity of event activities and social media data, and the data sparsity. This paper describes a systematic approach to detecting potential spatiotemporal patterns of events by resolving these challenges through several interrelated strategies: using kernel density estimation for smoothed social media intensity surfaces; utilizing event-unrelated social media posts to help map relative event prevalence; and normalizing event indicators based on historical fluctuation. This approach generates event indicator maps and significance maps explaining spatiotemporal variations of event prevalence to identify space-time regions with potentially abnormal event activities. The approach has been applied to detect influenza activity patterns in the conterminous US using Twitter data. A set of experiments demonstrated that our approach produces high-resolution influenza activity maps that could be explained by available ground truth data.  相似文献   

18.
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19.
Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.  相似文献   

20.
ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.  相似文献   

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