Xi Liu

Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associated analysis methods. The word sensing suggests two natures of the data. First, they can be viewed as the analogue and complement of remote sensing, as big data can capture well socioeconomic features while conventional remote sensing data do not have such privilege. Second, in social sensing data, each individual plays the role of a sensor. This article conceptually bridges social sensing with remote sensing and points out the major issues when applying social sensing data and associated analytics. We also suggest that social sensing data contain rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. In the coming big data era, GIScientists should investigate theories in using social sensing data, such as data representativeness and quality, and develop new tools to deal with social sensing data.

Revealing travel patterns and city structure with taxi trip data

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long- and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban sub-regions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate sub-region formation and the interaction patterns of center–local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.

Inferring trip purposes and uncovering travel patterns from taxi trajectory data

ABSTRACT Global positioning system-enabled vehicles provide an efficient way to obtain large quantities of movement data for individuals. However, the raw data usually lack activity information, which is highly valuable for a range of applications and services. This study provides a novel and practical framework for inferring the trip purposes of taxi passengers such that the semantics of taxi trajectory data can be enriched. The probability of points of interest to be visited is modeled by Bayes’ rules, which take both spatial and temporal constraints into consideration. Combining this approach with Monte Carlo simulations, we conduct a study on Shanghai taxi trajectory data. Our results closely approximate the residents’ travel survey data in Shanghai. Furthermore, we reveal the spatiotemporal characteristics of nine daily activity types based on inference results, including their temporal regularities, spatial dynamics, and distributions of trip lengths and directions. In the era of big data, we encounter the dilemma of “trajectory data rich but activity information poor” when investigating human movements from various data sources. This study presents a promising step toward mining abundant activity information from individuals’ trajectories.

Incorporating spatial interaction patterns in classifying and understanding urban land use

ABSTRACT Land use classification has benefited from the emerging big data, such as mobile phone records and taxi trajectories. Temporal activity variations derived from these data have been used to interpret and understand the land use of parcels from the perspective of social functions, complementing the outcome of traditional remote sensing methods. However, spatial interaction patterns between parcels, which could depict land uses from a perspective of connections, have rarely been examined and analysed. To leverage spatial interaction information contained in the above-mentioned massive data sets, we propose a novel unsupervised land use classification method with a new type of place signature. Based on the observation that spatial interaction patterns between places of two specific land uses are similar, the new place signature improves land use classification by trading off between aggregated temporal activity variations and detailed spatial interactions among places. The method is validated with a case study using taxi trip data from Shanghai.

Human mobility patterns in different communities: a mobile phone data-based social network approach

Detecting intensely connected sub-networks, or communities, from social networks has attracted much attention in social network studies. The widespread use of location-awareness devices provides a novel data source for constructing spatially embedded networks and uncovering spatial features of different population groups. Using an empirical mobile phone data-set, this paper attempts to explore the spatial distributions and human mobility patterns, as well as the interrelationship between them, at the community level. Three spatial patterns of communities are identified with the community detection algorithm and kernel density map method: single-centred distribution, dual-centred distribution and zonal distribution. We find different movement characteristics of these three community types by analysing angle distribution of trajectories and radius of gyration of users. Furthermore, we analyse spatial and temporal travel patterns for the users in dual-centred communities. The results indicate that people’s commuting travel brings about spatial interaction between urban district and suburbs, and verify our hypothesis that the distance decay effect along with social phenomena such as the home–work separation contributes to the formation of different community distributions.

Challenges for social flows

Abstract Social and interpersonal connections are attached to the built environment: people require physical infrastructure to meet and telecommunicate, and then populate these infrastructures with movement and information dynamics. In GIS analysis, actions are often represented as a unit of spatial information called the social flow–a linear geographic feature that evidences an individuals decision to connect places through travel, telecommunications and/or declaring personal relationships. These flows differ from traditional spatial networks (roads, etc.) because they are often non-planar, and unlike networks in operations systems (such as flight networks), provide evidence of personal intentionality to interact with the built environment and/or to perpetuate relationships with others. En masse, these flows sum to illustrate how humans, information and thoughts spread between and within places. Amid a growing abundance and usage of social flow data, we extend formal definitions of this data type, create new typologies, address new problems, and redefine social distance as the manifestation of social flows. Next, we outline challenges to fully leveraging these data with commercial GISystems by providing examples and potential solutions for representing, visualizing, manipulating, statistically analyzing and ascribing meaning to social flows. The goal of this discussion is to improve the dexterity of social flow data for geographic, environmental and social research questions.

Hidden style in the city: an analysis of geolocated airbnb rental images in ten major cities

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.n 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?n 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.

Wealthy Hubs and Poor Chains: Constellations in the U.S. Urban Migration System

Flows of people connect cities into complex systems. Urban systems research focuses primarily on creating economic models that explain movement between cities (whether people, telecommunications, goods or money), and more recently, finding strongly and weakly-connected regions. However, geometrically graphing the dependency between cities within a large network may reveal the roles of small and peripheral city agents in the system to show which cities switch regions from year to year, which medium-sized cities serve as collectors for large cities, and how the network is configured when connected by wealthy or deprived agents.

Using Yelp to Find Romance in the City: A Case of Restaurants in Four Cities

Romantic relationships are an understudied aspect of cities and the built environment. Yet, restaurants continue to attract couples and augment the landscape with visible signs of affection at a table for two---or more. User-generated content (UGC) of restaurant reviews from online review site Yelp (http://yelp.com) provide text on romantic keywords such as date, love, boyfriend, wife, anniversary, family by geolocated restaurants. We use these to distinguish restaurants and discover features of restaurants associated with various romantic keywords. These features include restaurant ratings and location, as well as comments about the ambiance, food, service, etc. Using data from the Yelp Dataset Challenge in U.S. cities Charlotte, NC, Las Vegas, NM, Phoenix, AZ, and Pittsburgh, PA, we employ different data mining and correlation tools as well as GIS modeling to learn more about what types of romantic relationships use which parts of the city, and how their choices of restaurants differ by relationship stage. We find that families prefer restaurants that are outside of the central business district (CBD), have good service and high-rated food, while couples---married or dating---prefer hot spots with great ambiance for nightlife. We also find that inexpensive food is not associated with romantic dates, and the quality of service also plays a secondary role to a classy and cozy atmosphere.