Hard and Soft Data Integration in Geocomputation: Mixed Methods for Data Collection and Processing in Urban Planning (chapter 4) In: Handbook on Planning Support Science. Ed. Stan Geertman & John Stillwell, Edward Elgar Publishers
The term “behavioral” has become a hot topic in recent years in various disciplines; however, there is yet limited understanding of what theories can be considered behavioral theories and what fields of research they can be applied to. Through a cross-disciplinary literature review, this article identifies sixty-two behavioral theories from 963 search results, mapping them in a diagram of four groups (factors, strategies, learning and conditioning, and modeling), and points to five discussion points: understanding of terms, classification, guidance on the use of appropriate theories, inclusion in data-driven research and agent-based modeling, and dialogue between theory-driven and data-driven approaches.
This introductory chapter establishes the context for subsequent contributions by outlining some of the major physical and social challenges that confront planners and policy-makers in different parts of the world. It then explains how the development of planning support systems has evolved into a much broader field of Planning Support Science which intersects with the emergence of data science, big data, data analytics and new urban science, thereby creating new opportunities for innovative solutions to support progress towards the development of smarter and more resilient urban futures. The structure of the book is clarified and short summary reviews of each chapter provide a composite portrait of the contents as a whole.
Given the present size of modern cities, it is beyond the perceptual capacity of most people to develop a good knowledge about the qualities of the urban space at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions such as ‘where the quality of the physical environment is the most dilapidated in the city that regeneration should be given first consideration’ and ‘in fast urbanising cities, how is the city appearance changing’. To address this issue, in the present study, we present a computer vision method that contains three machine learning models for the large-scale and automatic evaluation on the qualities of the urban environment by leveraging state-of-the-art machine learning techniques and wide-coverage street view images. From various physical qualities that have been identified by previous research to be important for the urban visual experience, we choose two key qualities, the construction and maintenance quality of building facade and the continuity of street wall, to be measured in this research. To test the validity of the proposed method, we compare the machine scores with public rating scores collected on-site from 752 passers-by at 56 locations in the city. We show that the machine learning models can produce a medium-to-good estimation of people's real experience, and the modelling results can be applied in many ways by researchers, planners and local residents.
In becoming a develop nation by 2020, Malaysia Government realized the need in providing affordable house to the public. Since Second Malaysia Plan, government has implemented various affordable housing projects and it continues until recent Malaysia Plan. To measure the effectiveness of the initiatives taken, public opinion is necessary. A social media platform has been seen as the most effective mechanism to get information on people’s thought and feeling towards certain issues. One of the best ways to extract emotions and thoughts from what people post in social media is through Sentiment Analysis (SA). This paper will propose a new framework that focuses on the application of sentiment analysis to assist the decision maker in understanding the real voice of the public in regard to property industry in Malaysia. The framework will consist of two components; sentiment classification at feature/aspect level and sentiment visualization to show the results of the analysis.
IntroductionComputation: the raster–pixel aproachCells: migrating from basic pixelsAgents: joining with cellsCells and agents in a computer's “artificial life”The hexa-dpi: closing the cycle in the digital ageConclusions
Urban land use change is a complex and dynamic process. It is therefore important to understand the complexity involved with system dynamics and choose appropriate modelling approaches. For this purpose, this paper firstly reviews how artificial intelligence (AI) approaches provide solutions to aid urban land dynamics modelling. The three dimensions that are considered pivotal for the understanding of urban dynamic processes - urban land dynamics, planning support and AI infrastructure - are defined. Once these three dimensions are clarified, it is possible to propose the different solution spaces provided by AI approaches using a graphic representation of a cube and its associated mathematical formulation. It is therefore possible to understand and define the best data model to represent the complexity of different phenomena in urban systems.
Given ongoing developments altering social and spatial cohesion in urban societies, a more comprehensive
understanding of segregation is needed. Taking the ‘mobilities turn’ at heart, we move
beyond place-based segregation approaches and focus on the practised urban experiences of individuals
through a more comprehensive assessment of their activity spaces. This study contributes
to people-based segregation research by mapping the activity spaces of individuals on the basis of
mobile phone data in Tallinn (Estonia) and relating these activity spaces to (mainly) the users’ ethnic
background (i.e. Estonian versus Russian). Significant ethnic differences in terms of (1) the
number of activity locations, (2) the geographical distribution of these locations, and (3) the overall
spatial extent of activity spaces are found. We also find that these differences tend to deepen
as the temporal framework is extended. We discuss the main implications for segregation
research and highlight some avenues for further research.
Most existing literature focuses on the exterior temporal rhythm of human
movement to infer the functional regions in a city, but they neglects the
underlying interdependence between the functional regions and human activities
which uncovers more detailed characteristics of regions. In this research, we
proposed a novel model based on the low rank approximation (LRA) to detect the
functional regions using the data from about 15 million check-in records during
a yearlong period in Shanghai, China. We find a series of latent structures,
called urban spatial-temporal activity structure (USTAS). While interpreting
these structures, a series of outstanding underlying associations between the
spatial and temporal activity patterns can be found. Moreover, we can not only
reproduce the observed data with a lower dimensional representative but also
simultaneously project both the spatial and temporal activity patterns in the
same coordinate system. By utilizing the K-means clustering algorithm, five
significant types of clusters which are directly annotated with a corresponding
combination of temporal activities can be obtained. This provides a clear
picture of how the groups of regions are associated with different activities
at different time of day. Besides the commercial and transportation dominant
area, we also detect two kinds of residential areas, the developed residential
areas and the developing residential areas. We further verify the spatial
distribution of these clusters in the view of urban form analysis. The results
shows a high consistency with the government planning from the same periods,
indicating our model is applicable for inferring the functional regions via
social media check-in data, and can benefit a wide range of fields, such as
urban planning, public services and location-based recommender systems and
Artificial intelligence (AI) systems are widely accepted as a technology offering an alternative way to tackle complex and dynamic problems in urban studies. The goal of this article is a review of current literature in the field of planning and AI. The aim of this review is to increase the understanding of how AI approaches urban and land dynamics modeling processes and how, as a result, researchers can structure that knowledge and choose the correct approaches to embed in their models. For this purpose, the authors review the applications of AI techniques in urban land dynamics domain as well as the emerging challenges they face. The authors discuss hybrid AI systems as a need resulting from the trend in planning policy to develop more holistic approaches. The authors conclude that, although challenges exist, AI-based approaches offer promising solutions for urban and land dynamics.
Sentiment analysis of microblogs such as Twitter has recently gained a fair
amount of attention. One of the simplest sentiment analysis approaches compares
the words of a posting against a labeled word list, where each word has been
scored for valence, -- a 'sentiment lexicon' or 'affective word lists'. There
exist several affective word lists, e.g., ANEW (Affective Norms for English
Words) developed before the advent of microblogging and sentiment analysis. I
wanted to examine how well ANEW and other word lists performs for the detection
of sentiment strength in microblog posts in comparison with a new word list
specifically constructed for microblogs. I used manually labeled postings from
Twitter scored for sentiment. Using a simple word matching I show that the new
word list may perform better than ANEW, though not as good as the more
elaborate approach found in SentiStrength.
Understanding public responses to environmental policies can help in achieving a move towards more renewable energy. Focusing on two types of public responses to a policy, namely public acceptance and public support, this study utilizes a survey of car owners (N = 1422) to explore public responses to an environmental transport policy in Sweden. The results demonstrate higher levels of public acceptance than support for the policy and that adopters of Alternative Fuel Vehicles (AFVs) are more prone to accept and support the policy by expressing higher intentions for continuous AFV adoption. Results of regression analyses show that policy acceptance can be explained by environmental beliefs and previous experience with AFVs. In addition, public support is also explained by public acceptance, even when controlling for other factors, which lends support to the deduction that policy acceptance can be theorized as antecedent to policy support. This study emphasizes the importance of understanding different types of public responses to an energy policy in order to recognize drivers for, and barriers against, successfully implementing a policy and communicating it with the public.
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implemen- tation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called dropout that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry
This paper explores the use of powerful new software tools and social media data that can be used to study the attitudes of people in urban places. In particular, it uses propensity scoring to develop matched pairs of mid-sized US cities in the North East and Midwest, where the most significant difference between each pair is that of population decline. This resulted in a group of fifty declining cities matched with fifty growing or stable cities. Over 300,000 Twitter posts were collected over the course of two months, each analysed for either positive or negative sentiment. After running difference-of-means tests, we found that sentiment in the declining cities does not differ in a statistically significant manner from that in stable and growing cities. These findings suggest that real opportunities exist the better to understand urban attitudes through sentiment analysis of Twitter data.
Along with the aging trend of the world population, the status and determinants of the travel behavior of the elderly have been gaining more attention in planning and research. Most of the existing research has focused on the influence of socio-demographics and built environments, while the impact of socio-cultural backgrounds has attracted less attention. Regarding the influence of built environments, previous studies have mainly focused on general elements such as density, and land use mixture, while specifics about how built environments influence the elderly have largely been ignored. This paper, therefore, attempts to investigate how socio-cultural settings, interacting with built environments, affect the travel behavior of the elderly in urban China. Particularly, we will examine the impacts of a set of built environment attributes on daily activity participation and the travel distance of the elderly in Nanjing. Based on quantitative and qualitative data, we found that special social and cultural contexts make the travel pattern of Chinese elderly and the determinants of that pattern different from those of their western counterparts. Specifically, it was found that public transportation accessibility instead of auto transportation accessibility, vegetable markets instead of supermarkets and convenience stores, open spaces and parks along with chess and card rooms instead of gyms and sports centers are more decisive in affecting the travel behavior of the elderly. These findings offer insights for policy making on distributing appropriate public facilities for the elderly in urban areas, especially in new towns in urban China.
In this research, a Bayesian network (BN) approach is proposed to model the car use behavior of drivers by time of day and to analyze its relationship with driver and car characteristics. The proposed BN model can be categorized as a tree-augmented naive (TAN) Bayesian network. A latent class variable is included in this model to describe the unobserved heterogeneity of drivers. Both the structure and the parameters are learned from the dataset, which is extracted from GPS data collected in Toyota City, Japan. Based on inferences and evidence sensitivity analysis using the estimated TAN model, the effects of each single observed characteristic on car use measures are tested and found to be significant. The features of each category of the latent class are also analyzed. By testing the effect of each car use measure on every other measure, it is found that the correlations between car use measures are significant and should be considered in modeling car use behavior.
Characterizing human mobility patterns is essential for understanding human
behaviors and the interactions with socioeconomic and natural environment. With
the continuing advancement of location and Web 2.0 technologies, location-based
social media (LBSM) have been gaining widespread popularity in the past few
years. With an access to locations of users, profiles and the contents of the
social media posts, the LBSM data provided a novel modality of data source for
human mobility study. By exploiting the explicit location footprints and mining
the latent demographic information implied in the LBSM data, the purpose of
this paper is to investigate the spatiotemporal characteristics of human
mobility with a particular focus on the impact of demography. We first collect
geo-tagged Twitter feeds posted in the conterminous United States area, and
organize the collection of feeds using the concept of space-time trajectory
corresponding to each Twitter user. Commonly human mobility measures, including
detected home and activity centers, are derived for each user trajectory. We
then select a subset of Twitter users that have detected home locations in the
city of Chicago as a case study, and apply name analysis to the names provided
in user profiles to learn the implicit demographic information of Twitter
users, including race/ethnicity, gender and age. Finally we explore the
spatiotemporal distribution and mobility characteristics of Chicago Twitter
users, and investigate the demographic impact by comparing the differences
across three demographic dimensions (race/ethnicity, gender and age). We found
that, although the human mobility measures of different demographic groups
generally follow the generic laws (e.g., power law distribution), the
demographic information, particular the race/ethnicity group, significantly
affects the urban human mobility patterns.
To achieve valid conclusions, studies exploring associations of the built environment with residents' physical activity and health-related outcomes need to employ statistical approaches accounting for clustered data. This article discusses the following main statistical approaches: analysis of covariance, regression models with robust standard errors, generalized estimating equations, and multilevel generalized linear models. The choice of a statistical method depends on the characteristics of the study and research questions. While the first three approaches are employed to account for clustering in the data, multilevel models can also help unravel more substantive issues within a social ecological theoretical framework of health behavior.
We introduce a novel scheme for automatically deriving synthetic walking (locomotion) and movement (steering and avoidance) behavior in simulation from simple trajectory samples. We use a combination of observed and recorded real-world movement trajectory samples in conjunction with synthetic, agent-generated, movement as inputs to a machine-learning scheme. This scheme produces movement behavior for non-sampled scenarios in simulation, for applications that can differ widely from the original collection settings. It does this by benchmarking a simulated pedestrian's relative behavioral geography, local physical environment, and neighboring agent-pedestrians; using spatial analysis, spatial data access, classification, and clustering. The scheme then weights, trains, and tunes likely synthetic movement behavior, per-agent, per-location, per-time-step, and per-scenario. To prove its usefulness, we demonstrate the task of generating synthetic, non-sampled, agent-based pedestrian movement in simulated urban environments, where the scheme proves to be a useful substitute for traditional transition-driven methods for determining agent behavior. The potential broader applications of the scheme are numerous and include the design and delivery of location-based services, evaluation of architectures for mobile communications technologies, what-if experimentation in agent-based models with hypotheses that are informed or translated from data, and the construction of algorithms for extracting and annotating space-time paths in massive data-sets.
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