New Paper: Assessing the Placeness of Locations through User-contributed Content

In the past we have written about how one can use crowdsourced data to gain a collective sense of place from Twitter contributions and also from corresponding Wikipedia entries (e.g. here). In a new paper with Xiaoyi Yuan, we extend this work to explore how user-contributed data can be used to explore if urban places are becoming inauthentic due to urban commodification and standardization by chain stores such as restaurants. To this end, at the at 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI) we have a paper entitled: "Assessing the Placeness of Locations through User-contributed Content"

In the paper we attempt to understand the relationship between restaurants and urban identities via user-contributed content. We extracted and analyzed information from over 3 million Yelp reviews from 37,000 restaurants using a Convolutional Neural Network (CNN) model in order to study places from the bottom up. Specifically we were interested to what extent cities share similarities or differences in their Yelp restaurant reviews. Furthermore, we wanted to explore how opinion aspects (i.e. what reviewers care about the most) are mentioned differently in urban chain and independent restaurants. Through the analysis of the Yelp reviews we find that online geo-tagged text data is fruitful for understanding places and aspect-based sentiment analysis helps us understand the large volumes of text. Not only did we discover that cities show homogeneity in terms of restaurant reviews, but for chain restaurants, “location” often emphasizes the differences between different stores of the same chain whereas for independent restaurant reviews, the aspect “location” reflects the characteristics of the places the restaurants are situated. If this is of interest to you, below we provide the abstract to the paper, along with some of the key findings and a link to the paper.

Abstract

Previous research has argued that urban places are becoming “placeless” and inauthentic. Many local policies have also proposed to encourage more independent stores in order to restore urban identity. Others argue, however, that chain stores provide affordable merchandise and different locations of the same chain may have different meanings to an individual. The research presented in this paper uses a Convolutional Neural Networks model to extract opinion aspects from more than 3 million user-contributed Yelp restaurant reviews. The results show high homogeneity among cities in terms of the average proportions of aspects in restaurant reviews. In addition, for fast food chains, “location” is the only aspect category reviewed proportionally higher than independent fast food restaurants. An analysis of the co-occurrences of “location” indicates that the identity of chain restaurants stems from the comparison between the same chain of different locations whereas the identity of the independent restaurants is more diverse, implying the intricacies of placeness of urban stores. This research demonstrates that fine-grained sentiment analysis (i.e., opinion aspect extraction and analysis) with geo-tagged text data is fruitful for studying nuanced place perceptions on a large scale.

KEYWORDS: Urban Places, Convolutional Neural Networks, Aspect-based Sentiment Analysis

Figure 1: Illustration of an example of a CNN layer.

Figure 3: Mapping restaurants in NV, AZ, PA, NC, WI, IL. Not all cities are shown in each state. Only cities have data that accounts for the majority of the restaurants in that state are mapped, for the sake of visual clarity.

Figure 6: Average proportions of aspect categories for chain and independent fast food restaurants for two kinds of cuisine (American, Mexican) in Las Vegas, Phoenix, and Charlotte, normalized by dividing the mean for comparison.

Reference:

Yuan X. and Crooks A.T. (2019), Assessing the Placeness of Locations through User-contributed Content, in Gao, S., Newsam, S., Zhao, L., Lunga, D., Hu, Y., Martins, B., Zhou, X. and Chen, F. (eds.), Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI), Chicago, IL. pp. 15-23. (pdf)

Place-Based Simulation Modelling

Nick Malleson, Alison Heppenstall and myself recently had a chapter published in the Oxford Research Encyclopedia of Criminology and Criminal Justice, entitled "Place-Based Simulation Modelling:  Agent-Based Modelling and Virtual Environments". In the chapter, we discuss how agent-based modeling (ABM) can be used for modeling spatial patterns of crime.

Specifically we discuss the motivation for ABM in criminology. We then introduce the core components of ABM and how one can structure such models and represent human behavior in them (e.g. using Beliefs-Desires-Intentions (BDI) or Physical conditions, Emotional states, Cognitive capabilities and Social status" (PECS) frameworks). Next, we discuss space can be represented in agent-based models, both from an abstract sense but also accurate spatial environments (i.e. by using GIS data). The chapter then moves onto briefly discussing tools for the implementation of agent-based models (e.g. MASON, NetLogo, Repast) before we provide a critique of ABM for modeling spatial crime, including its appeal (e.g. the emergence of crime from the bottom up), the difficulties of using ABM (e.g adequately defining behavior, access to data etc.) and finally the ethical implications of using agent-based models for studying crime. Below you can read the official summary of the chapter along with its full citation.

Chapter Summary: 

Since the earliest geographical explorations of criminal phenomena, scientists have come to the realization that crime occurrences can often be best explained by analysis at local scales. For example, the works of Guerry and Quetelet—which are often credited as being the first spatial studies of crime—analyzed data that had been aggregated to regions approximately similar to US states. The next major seminal work on spatial crime patterns was from the Chicago School in the 20th century and increased the spatial resolution of analysis to the census tract (an American administrative area that is designed to contain approximately 4,000 individual inhabitants). With the availability of higher-quality spatial data, as well as improvements in the computing infrastructure (particularly with respect to spatial analysis and mapping), more recent empirical spatial criminology work can operate at even higher resolutions; the “crime at places” literature regularly highlights the importance of analyzing crime at the street segment or at even finer scales. These empirical realizations—that crime patterns vary substantially at micro places—are well grounded in the core environmental criminology theories of routine activity theory, the geometric theory of crime, and the rational choice perspective. Each theory focuses on the individual-level nature of crime, the behavior and motivations of individual people, and the importance of the immediate surroundings. For example, routine activities theory stipulates that a crime is possible when an offender and a potential victim meet at the same time and place in the absence of a capable guardian. The geometric theory of crime suggests that individuals build up an awareness of their surroundings as they undertake their routine activities, and it is where these areas overlap with crime opportunities that crimes are most likely to occur. Finally, the rational choice perspective suggests that the decision to commit a crime is partially a cost-benefit analysis of the risks and rewards. To properly understand or model these three decisions it is important to capture the motivations, awareness, rationality, immediate surroundings, etc., of the individual and include a highly disaggregate representation of space (i.e. “micro-places”). Unfortunately one of the most common methods for modeling crime, regression, is somewhat poorly suited capturing these dynamics. As with most traditional modeling approaches, regression models represent the underlying system through mathematical aggregations. The resulting models are therefore well suited to systems that behave in a linear fashion (e.g., where a change in model input leads to a predictable change in the model output) and where low-level heterogeneity is not important (i.e., we can assume that everyone in a particular group of people will behave in the same way). However, as alluded to earlier, the crime system does not necessarily meet these assumptions. To really understand the dynamics of crime patterns, and to be able to properly represent the underlying theories, it is necessary to represent the behavior of the individual system components (i.e. people) directly. For this reason, many scientists from a variety of different disciplines are turning to individual-level modeling techniques such as agent-based modeling.

Keywords: agent-based modelling, crime simulation, travel to crime, virtual environment, NetLogo, virtual laboratory.

Figure 1: The process of initializing, running, and analyzing an agent-based model.

Full Reference:

Malleson, N., Heppenstall, A. and Crooks, A.T. (2018). Place-Based Simulation Modelling: Agent-Based Modelling and Virtual Environments, Oxford Research Encyclopedia of Criminology and Criminal Justice, Oxford University Press. DOI: 10.1093/acrefore/9780190264079.013.319 (pdf)

New Paper: User-Generated Big Data and Urban Morphology

Continuing our work with crowdsourcing and geosocial analysis we recently had a paper published in a special issue of the  Built Environment journal entitled "User-Generated Big Data and Urban Morphology."

The theme of the special issue is: "Big Data and the City" which was guest edited by Mike Batty and includes 12 papers.  To quote from the website: 

"This cutting edge special issue responds to the latest digital revolution, setting out the state of the art of the new technologies around so-called Big Data, critically examining the hyperbole surrounding smartness and other claims, and relating it to age-old urban challenges. Big data is everywhere, largely generated by automated systems operating in real time that potentially tell us how cities are performing and changing. A product of the smart city, it is providing us with novel data sets that suggest ways in which we might plan better, and design more sustainable environments. The articles in this issue tell us how scientists and planners are using big data to better understand everything from new forms of mobility in transport systems to new uses of social media. Together, they reveal how visualization is fast becoming an integral part of developing a thorough understanding of our cities."

Table of Contents

In our paper we discuss and show how crowdsourced data is leading to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics. Specifically how such data can provide us information pertaining to linked spaces and geosocial neighborhoods. We argue that a geosocial neighborhood is not defined by its administrative boundaries, planning zones, or physical barriers, but rather by its emergence as an organic self-organized social construct that is embedded in geographical spaces that are linked by human activity. Below is the abstract of the paper and some of the figures we have in it which showcase our work.

"Traditionally urban morphology has been the study of cities as human habitats through the analysis of their tangible, physical artefacts. Such artefacts are outcomes of complex social and economic forces, and their study is primarily driven by traditional modes of data collection (e.g. based on censuses, physical surveys, and mapping). The emergence of Web 2.0 and through its applications, platforms and mechanisms that foster user-generated contributions to be made, disseminated, and debated in cyberspace, is providing a new lens in the study of urban morphology. In this paper, we showcase ways in which user-generated ‘big data’ can be harvested and analyzed to generate snapshots and impressionistic views of the urban landscape in physical terms. We discuss and support through representative examples the potential of such analysis in revealing how urban spaces are perceived by the general public, establishing links between tangible artefacts and cyber-social elements. These links may be in the form of references to, observations about, or events that enrich and move beyond the traditional physical characteristics of various locations. This leads to the emergence of alternate views of urban morphology that better capture the intricate nature of urban environments and their dynamics."

Keywords: Urban Morphology, Social Media, GeoSocial, Cities, Big Data.

City Infoscapes – Fusing Data from Physical (L1, L2), Social, Perceptual (L3) Spaces to Derive Place Abstractions (L4) for Different Locations (N1, N2).

Recreational Hotspots Composed of “Locals” and “Tourists” with Perceived Artifacts Indicating “Use” and “Need”. (A) High Line Park (B) Madison Square Garden.

Moving from Spatial Neighborhoods to Geosocial Neighborhoods via Links.

The Emergence of Geosocial Neighborhoods after the in the
Aftermath of the 2013 Boston Marathon Bombing

Full  Reference: 

Crooks, A.T., Croitoru, A., Jenkins, A., Mahabir, R., Agouris, P. and Stefanidis A. (2016). “User-Generated Big Data and Urban Morphology,”  Built Environment, 42 (3): 396-414. (pdf)

Crowdsourcing A Collective Sense of Place

Following on with our GeoSocial Analysis work, we recently had a paper published in  PLOS ONE entitled "Crowdsourcing A Collective Sense of Place." In the paper we discuss and showcase how one can take a quantitative approach to derive a collective sense of place from Twitter contributions and also from corresponding Wikipedia entries.

To illustrate this we present a brief study three cities, that of New York, Los Angeles and Singapore. Below you read the abstract of the paper, see some images from the paper especially the flowchart describing overall process used to discover platial alignment, along with the full reference to the paper.

Place can be generally defined as a location that has been assigned meaning through human experience, and as such it is of multidisciplinary scientific interest. Up to this point place has been studied primarily within the context of social sciences as a theoretical construct. The availability of large amounts of user-generated content, e.g. in the form of social media feeds or Wikipedia contributions, allows us for the first time to computationally analyze and quantify the shared meaning of place. By aggregating references to human activities within urban spaces we can observe the emergence of unique themes that characterize different locations, thus identifying places through their discernible sociocultural signatures. In this paper we present results from a novel quantitative approach to derive such sociocultural signatures from Twitter contributions and also from corresponding Wikipedia entries. By contrasting the two we show how particular thematic characteristics of places (referred to herein as platial themes) are emerging from such crowd-contributed content, allowing us to observe the meaning that the general public, either individually or collectively, is assigning to specific locations. Our approach leverages probabilistic topic modelling, semantic association, and spatial clustering to find locations are conveying a collective sense of place. Deriving and quantifying such meaning allows us to observe how people transform a location to a place and shape its characteristics.  

Keywords: Online Encyclopedias, Twitter, Data Visualization, Social Media, Semantics.

Flowchart describing overall process used to discover platial alignment.

Statistically significant clusters of recreation and entertainment categories concentrated over Manhattan, NYC

Maps depict significant hotspots for each of the high-level categories for (A) Singapore, (B) London, (C) Los Angeles, and (D) New York City.

Full Reference: 

Jenkins A., Croitoru, A. Crooks, A.T. and Stefanidis, A. (2016), Crowdsourcing A Collective Sense of Place, PLoS ONE 11(4): e0152932. doi:10.1371/journal.pone.0152932

Crowdsourcing Urban Form and Function

We have just had published a new paper entitled: "Crowdsourcing Urban Form and Function" in International Journal of Geographical Information Science which showcases some of our recent work with respect to cities and how new sources of information can be used to study urban morphology at a variety of spatial and temporal scales. Below is the abstract for the paper: 

"Urban form and function have been studied extensively in urban planning and geographic information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Towards this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies work to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing and modeling form and function for urban design and analysis."

This paper builds and extends considerably our prior work, with respect to crowdsourcing, volunteered and ambient geographic information. In the scope of this paper we use the term ‘urban form’ to refer to the aggregate of the physical shape of the city, its buildings, streets, and all other elements that make up the urban space. In essence, the geometry of the city. In contrast, we use the term ‘urban function’ to refer to the activities that are taking place within this space. To this end we contrast how crowdsourced data can related to more traditional sources of such information both explicitly and implicitly as shown in the table below. 

A typology of implicit and explicit form and function content

In addition, we also discuss in the paper how these new sources of data, which are often at finer resolutions than more authoritative data are allowing us to to customize the we we aggregate the data  at various geographical levels as shown below. Such aggregations can range from building footprints and addresses to street blocks (e.g. for density analysis), or street networks (e.g. for accessibility analysis). For large-scale urban analysis we can revert to the use of zonal geographies or grid systems.  

Aggregation methods for varied scales of built environment analysis

In the application section of the paper we highlight how we can extract implicit form and function from crowdsourced data. The image below for example, shows how we can take information from Twitter, and differentiate different neighborhoods over space and time.

Neighborhood map and topic modeling results showing the mixture of social functions in each area.

Finally in the paper, we outline an emerging research agenda related to the "persistent urban morphology concept" as shown below. Specifically how crowdsourcing is changing how we collect, analyze and model urban morphology. Moreover, how this new paradigm provides a new lens for studying the conceptualization of how cities operate, at much finer temporal, spatial, and social scales than we had been able to study so far.

The persistent urban morphology concept.

We hope you enjoy the paper.

Full Reference:  

Crooks, A.T., Pfoser, D., Jenkins, A., Croitoru, A., Stefanidis, A., Smith, D. A., Karagiorgou, S., Efentakis, A. and Lamprianidis, G. (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905 (pdf)