A spatial analysis of non‐English Twitter activity in Houston,TX

The use of social media data in geographic studies has become common, yet the question of social media's validity in such contexts is often overlooked. Social media data suffers from a variety of biases and limitations; nevertheless, with a proper understanding of the drawbacks, these data can be powerful. As cities seek to become “smarter,” they can potentially use social media data to creatively address the needs of their most vulnerable groups, such as ethnic minorities. However, questions remain unanswered regarding who uses these social networking platforms, how people use these platforms, and how representative social media data is of users' everyday lives. Using several forms of regression, I explore the relationships between a conventional data source (the U.S. Census) and a subset of Twitter data potentially representative of minority groups: tweets created by users with an account language other than English. A considerable amount of non‐stationarity is uncovered, which should serve as a warning against sweeping statements regarding the demographics of users and where people prefer to post. Further, I find that precisely located Twitter data informs us more about the digital status of places and less about users' day‐to‐day travel patterns.     

Crowdsourcing functions of the living city from Twitter and Foursquare data

ABSTRACT

Urban functions are closely related to people’s spatiotemporal activity patterns, transportation needs, and a city’s business distribution and development trends. Studies investigating urban functions have used different data sources, such as remotely sensed imageries, observation, photography, and cognitive maps. However, these data sources usually suffer from low spatial, temporal, and thematic resolution. This article attempts to investigate human activities to understand urban functions through crowdsourcing social media data. In this study, we mined Twitter and Foursquare data to extract and analyze six types of human activities. The spatiotemporal analysis revealed hotspots for different activity intensities at different temporal resolution. We also applied the classified model in a real-time system to extract information of various urban functions. This study demonstrates the significance and usefulness of social sensing in analyzing urban functions. By combining different platforms of social media data and analyzing people’s geo-tagged city experience, this article contributes to leverage voluntary local knowledge to better depict human dynamics, discover spatiotemporal city characteristics, and convey information about cities.     

Understanding the interplay between bus,metro, and cab ridership dynamics in Shenzhen,China

The most common mass transit modes in metropolitan cities include buses, subways, and taxicabs, each of which contribute to an interconnected complex network that delivers urban dwellers to their destinations. Understanding the intertwined usages of these three transit modes at different places and time allows for better sensing of urban mobility and the built environment. In this article, we leverage a comprehensive data collection of bus, metro, and taxicab ridership from Shenzhen, China to unveil the spatio‐temporal interplay between different mass transit modes. To achieve this goal, we develop a novel spectral clustering framework that imposes spatio‐temporal similarities between mass transit mode usage in urban space and differentiates urban spaces associated with distinct ridership patterns of mass transit modes. Five resulting categories of urban spaces are identified and interpreted with auxiliary knowledge of the city's metro network and land‐use functionality. In general, different categorized urban spaces are associated with different accessibility levels (such as high‐, medium‐, and low‐ranked) and different urban functionalities (such as residential, commercial, leisure‐dominant, and home–work balanced). The results indicate that different mass transit modes cooperate or compete based on demographic and socioeconomic attributes of the underlying urban environments. Our proposed analytical framework provides a novel and effective way to explore the mass transit system and the functional heterogeneity in cities. It demonstrates great potential for assisting policymakers and municipal managers in optimizing public transportation facility allocation and city‐wide daily commuting distribution.     

Visualizing population mobility from spatiotemporally aggregated mobile phone data via a 3D gradient approach

Population mobility patterns are important for understanding a city's rhythms. With the widespread use of mobile phones, population-based trajectories can be utilized to explore such mobility patterns. However, to protect personal privacy, mobile phone data must be de-identified by data aggregation within each spatiotemporal unit. In data acquired from mobile phones, population mobility features are still implicit in the spatiotemporally aggregated grid data. In this study, based on image-processing techniques, a two-step 3D gradient method is adopted to extract the movement features. The first step is to estimate the initial movement pattern in each spatiotemporal grid, and then to estimate the accumulated movement pattern within a time period around a geographical grid. This method can be applied adaptively to multi-scale spatiotemporal grid data. Using geospatial visualization methods, estimated motion characteristics such as velocity and flow direction can be made intuitive and integrated with other multiscale geospatial data. Furthermore, the correlation between the population mobility pattern and demographic characteristics, such as gender and age groups, can be analyzed with intuitive visualization. The implication of the visualization results can be used for understanding the human dynamics in a city, which can be beneficial for urban planning, transportation management, and socioeconomic development.     

Exploring the effectiveness of location‐based social media in modeling user activity space: A case study of Weibo

Location‐based social media (LBSM) has been widely utilized to supplement traditional survey methods in modeling human activity patterns. However, there has not been sufficient study to assess the reliability of these data in deriving human movement. This research aims to evaluate how data collection duration and sample sizes affect the reliability of LBSM data in activity modeling based on two indicators: radius of gyration (ROG) and entropy. We use a linear regression model with logarithmic transformation to approximate how the magnitude of each indicator changes with different data collection durations—from 1 to 12 months. The results indicate that both ROG and entropy increase when the amount of data increases. However, the rate of increase slows down and approaches zero eventually. We also approximated the limit values and verified that with 12‐month data, we are at approximately >95% magnitude of the limit values for both indicators in all three cities. The clustering analysis also demonstrated that there are outlier users who exhibit distinct patterns. This case study focuses on three Chinese cities (Beijing, Shanghai, and Guangzhou) and provides a useful reference to explore the balance point between data effectiveness and an appropriate sample size from LBSM data.     

Evaluation of the quality of an online geocoding resource in the context of a large Brazilian city

Geocoding urban addresses usually requires the use of an underlying address database. Under the influence of the format defined for TIGER files decades ago, most address databases and street geocoding algorithms are organized around street centerlines, associating numbering ranges to thoroughfare segments between two street crossings. While this method has been successfully employed in the USA for a long time, its transposition to other countries may lead to increased errors. This article presents an evaluation of the centerline‐geocoding resources provided by Google Maps, as compared to the point‐geocoding method used in the city of Belo Horizonte, Brazil, which we took as a baseline. We generated a textual address for each point object found in the city's point‐based address database, and submitted it to the Google Maps geocoding API. We then compared the resulting coordinates with the ones recorded in Belo Horizonte's GIS. We demonstrate that the centerline segment interpolation method, employed by the online resources following the American practice, has problems that can considerably influence the quality of the geocoding outcome. Completeness and accuracy have been found to be irregular, especially within lower income areas. Such errors in online services can have a significant impact on geocoding efforts related to social applications, such as public health and education, since the online service can be faulty and error‐prone in the most socially demanding areas of the city. In the conclusion, we point out that a volunteered geographic information (VGI) approach can help with the enrichment and enhancement of current geocoding resources, and can possibly lead to their transformation into more reliable point‐based geocoding services.     

Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.     

城市街区和签到数据结合的个性化城市兴趣区域推荐方法

利用社交媒体用户的历史签到数据分析用户空间活动偏好实现用户兴趣区域推荐,在城市商业规划中起着重要作用,也为了解人们的城市生活和需求提供帮助。已有方法获得的ROI具有模糊性和多样性,无法给ROI赋予准确的地理描述信息,对用户来说可解释性不强。因此,本文提出了一种结合城市街区和签到数据的个性化兴趣区域推荐方法(CBCD),引入城市街区概念解决ROI边界模糊问题。首先,通过城市道路网生成城市街区,并将大规模签到数据映射到城市街区转换为区域签到;然后,基于区域签到对用户空间活动偏好和类别偏好分别进行个性化建模;最后,融合空间和类别活动偏好,向用户推荐其可能感兴趣的区域。在真实的数据集上进行试验,结果表明该方法具有较高的推荐精度,对用户感兴趣城市街区的挖掘和推荐具有一定的价值。     

A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

ABSTRACT

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation. First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e. endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.     

Natural Conversational Interfaces to Geospatial Databases

Natural (spoken) language, combined with gestures and other human modalities, provides a promising alternative for interacting with computers, but such benefit has not been explored for interactions with geographical information systems. This paper presents a conceptual framework for enabling conversational human‐GIS interactions. Conversations with a GIS are modeled as human‐computer collaborative activities within a task domain. We adopt a mental state view of collaboration and discourse and propose a plan‐based computational model for conversational grounding and dialogue generation. At the implementation level, our approach is to introduce a dialogue agent, GeoDialogue, between a user and a geographical information server. GeoDialogue actively recognizes user's information needs, reasons about detailed cartographic and database procedures, and acts cooperatively to assist user's problem solving. GeoDialogue serves as a semantic ‘bridge’ between the human language and the formal language that a GIS understands. The behavior of such dialogue‐assisted human‐GIS interfaces is illustrated through a scenario simulating a session of emergency response during a hurricane event.     

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

Traffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning.     

融合图像和时空信息的社交媒体用户活动分类方法

社交媒体签到数据中蕴含着大量的用户活动信息。理解社交媒体用户的活动和行为类型,对探索人类的移动性和行为模式等有着重要意义。提出了一种针对新浪微博（简称为微博）的用户活动分类方法,结合图像表达和时空数据分类技术,识别微博签到数据所代表的用户活动类型。首先,根据兴趣点属性信息将微博签到数据所代表的用户活动分为餐饮、生活服务、校园、户外、娱乐、出行6大类;然后,基于卷积神经网络和K近邻分类方法,融合签到数据中的图像场景信息与时空信息,对微博用户的活动行为进行分类。实验结果表明,所提方法能够显著提高微博用户活动类型识别的准确性,为精确探索人类行为活动提供更加有效的数据支持。     

Geovisualization of Human Hybrid Activity-Travel Patterns

While GIS‐based geovisualization techniques have been fruitfully employed in a wide range of fields, they have not been applied to explore human hybrid activity‐travel patterns (i.e. the patterns of people's activities and/or travel in both the physical and virtual worlds). This study develops two geovisualization approaches using 3D and 2D GIS techniques to address the difficulties faced by past studies. First, the notions of information cube and hybrid 3D space‐time paths are developed to accommodate cyberspatial activities in exploratory data analysis. Second, a new representation of 2D space‐time paths that incorporates parallel coordinate plots is developed for exploring the multiple attributes of people's Internet and physical activities. These two methods were implemented using ArcObjects and the Visual Basic for Applications environment in ArcGIS. They were used to visualize a complex Internet‐activity diary dataset collected in Columbus (Ohio, USA). The study shows that the 3D and 2D geovisualization methods developed allow us to uncover important patterns in hybrid human activity‐travel behavior.     

Geo-Pragmatics for the Geospatial Semantic Web

Geo‐Pragmatics is introduced here as an enhanced representation for ontologies in which geospatial, geographical and geoscientific concepts are not only defined, but their pragmatic context is also captured and potentially reasoned with. A framework for representing such context is developed using three core aspects: dimensions, agents and roles. Dimensions consist of a concept's origins, uses and effects; these are generated by the interaction of human, machine and natural agents, and involve entities with roles developed from method‐driven perspectives and epistemic‐driven versions. The relationship between these core aspects is explored conceptually and implications for geoscientific ontologies are discussed, including identification of a basic ontological type, the situated concept, whose meaning is defined by its geographical‐historical context. Geo‐pragmatics should help geoscientists evaluate the scientific merit, and fitness for scientific use, of geoscientific ontologies in emerging e‐science initiatives.     

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

Reducing domestic abuse has become a priority for both local and national governments in the UK, with its substantial human, social, and economic costs. It is an interdisciplinary issue, but to date there has been no research in the UK that has focused on neighborhood‐level predictors of domestic abuse and their variation across space. This article uses geographically weighted regression to model the predictors of police‐reported domestic abuse in Essex. Readily available structural and cultural variables were found to predict the domestic abuse rate and the repeat victimization rate at the lower super output area level and the model coefficients were all found to be non‐stationary, indicating varying relationships across space. This research not only has important implications for victims' well being, but also enables policy makers to gain a better understanding of the geography of victimization, allowing targeted policy interventions and efficiently allocated resources.     

Modeling and Communicating the Conceptual Intent of Geo‐Analytical Tasks for Human‐GIS Interaction

One of the fundamental issues of geographical information science is to design GIS interfaces and functionalities in a way that is easy to understand, teach, and use. Unfortunately, current geographical information systems (including ArcGIS) remains very difficult to use as spatial analysis tools, because they organize and expose functionalities according to GIS data structures and processing algorithms. As a result, GIS interfaces are conceptually confusing, cognitively complex, and semantically disconnected from the way human reason about spatial analytical activities. In this article, we propose an approach that structures GIS analytical functions based on the notion of “analytical intent”. We describe an experiment that replaces ArcGIS desktop interface with a conversational interface, to enable mixed‐initiative user‐system interactions at the level of analytical intentions. We initially focus on the subset of GIS functions that are relevant to “finding what's inside” as described by Mitchell, but the general principles apply to other types of spatial analysis. This work demonstrates the feasibility of delegating some spatial thinking tasks to computational agents, and also raises future research questions that are key to building a better theory of spatial thinking with GIS.     

'Situating' Simulation to Model Human Spatio-Temporal Interactions: An Example Using Crime Events

Many social phenomena have a spatio‐temporal dimension and involve dynamic decisions made by individuals. In the past, researchers have often turned to geographic information systems (GIS) to model these interactions. Although GIS provide a powerful tool for examining the spatial aspects of these interactions, they are unable to model the dynamic, individual‐level interactions across time and space. In an attempt to address these issues, some researchers have begun to use simulation models. But these models rely on artificial landscapes that do not take into account the environment in which humans move and interact. This research presents the methodology for ‘situating’ simulation through the use of a new modeling tool, Agent Analyst, which integrates agent‐based modeling (ABM) and GIS. Three versions of a model of street robbery are presented to illustrate the importance of using ‘real’ data to inform agent activity spaces and movement. The successful implementation of this model demonstrates that: (1) agents can move along existing street networks; (2) land use patterns can be used to realistically distribute agent's homes and activities across a city; and (3) the incidence and pattern of street robberies is significantly different when ‘real’ data are used.     

Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing

Floating Car Data (FCD) refers to the trajectories of vehicles equipped with Global Positioning System-enabled devices that automatically record location-related data within a short time interval. As taxies in Chinese cities continually drive along the streets seeking passengers, FCD can easily traverse the entire street network in a city on a daily basis. Taking advantage of this situation, this study extracted passenger pickup and drop-off locations from FCD sourced from 6445 taxis over a 2-week period in Nanjing, China to discover human behavioral patterns and the dynamics behind them. In this study, road nodes are converted to the points, based on which Thiessen polygons are generated to divide the study area into small areas with the goal of exploring the spatial distribution of pickup and drop-off locations. Moran’s I index is used to calculate the spatial autocorrelation of the spatial distribution of pickup and drop-off locations, and hot spot analysis is used to identify statistically significant spatial clusters of hot and cold spots. The spatial and temporal patterns of FCD in the study area are investigated, and the results show that: (1) the temporal patterns show a strong daily rhythm, (2) the spatial patterns show that the number of pickup and drop-off locations gradually diminish from the downtown areas to the outer suburbs, (3) the spatiotemporal patterns exhibit large differences over time, and (4) the driving forces explored by regression models indicate that population density and transportation density are consistent with the population distribution, but per capita disposable income is not consistent with the population distribution.     

基于社交媒体的城市污染信息探测

了解城市污染状况是治理城市污染的首要环节。社交媒体中包含了能够反映人们对周围环境的感受的数据资料,可以帮助人们更直观地了解城市污染现状。本文以新浪微博为例,提出了从社交媒体中获取城市污染相关数据的方案,设计了基于社交媒体的城市污染信息分类法和可视化方法,在实验部分以北京市为例对本方法的结果进行了分析与讨论。     

资源三号卫星高分影像的城市建筑物阴影提取

针对城市高分影像中普遍存在的建筑物阴影严重影响了高分影像的处理和应用问题,该文通过对资源三号卫星(ZY-3)各波段的影像特征进行分析,分别采用基于直方图的阈值分割法、基于主成分变换和HIS变换集成法对阴影提取并对比分析,在此基础上提出了一种基于谱间关系的阴影提取模型。实验结果表明,该文提出的方法能够较好地区分ZY-3影像中的阴影和偏蓝色地物及水体,能够准确地提取城市高大地物阴影。