Research on urban travel represents an important area in geography, transportation planning and urban studies. Compared to the traditional trip based approach, the activity based approach offers a better understanding of the motivations underlying travel, that is, activity participation. Urban activity-travel is complex as it takes place both in space and time. Building upon the time geography framework, this study provides new ways to characterize urban activity-travel and examine the association with accessibility. A new optimization model incorporating complex urban travel is also proposed for service provision planning. Activity space represents an important concept for understanding human activity-travel. The geometry based approaches widely used for delineating activity spaces are limited in fully characterizing real-world travel behavior. To address the issue, Chapter 2 proposes a new time geography based approach to more accurately portray urban activity spaces. The proposed approach takes into account the full complexity of real-world travel and underlying urban structures. Results of an empirical study are presented based on the 2008 Add-on National Household Travel Survey conducted in Tucson, Arizona. Activity spaces of 1,164 sample travelers are delineated and analyzed. Results show the effectiveness of the new approach in more realistically depicting urban activity-travel. Understanding the impact of the built environment on travel is important for formulating effective travel reduction policies. In Chapter 3, a study is presented to examine the relationship between accessibility to urban opportunities and urban travel. Activity spaces are drawn to characterize the spatial extent of activity-travel, and a new accessibility measure is introduced to account for the complexity of urban travel. An empirical study based on a travel survey dataset in Tucson, Arizona shows that improved accessibility is generally associated with reduced travel, but such an effect varies across different activity types. In addition, employment status and trip-chaining behavior can be used to explain the varying influences on the accessibility-travel relationship. In Chapter 4, a new multi-objective location model is developed with the goal of accessibility maximization. The model extends the classic p-median problem (PMP) to account for accessibility in a more realistic manner. Trip chaining and activity space are incorporated into the location model. In addition to fixed home locations, stops along chained trips are allowed for potential service site visits. The model is applied to locate service facilities in Tucson, AZ. Alternative versions of the objective function are solved exactly with the resulting sets of optimal facility locations displayed and analyzed. Decision makers are given flexibility to determine the relative importance for each of three sub-objective, based on the type of services being located, their preferences and practical needs.

There is ever greater recognition in the social and health sciences that neighborhood context should be incorporated in studying human behavior. In terms of analyzing urban human behavior, activity space data (i.e. data associated with an individual's home and routine activity locations) can offer diverse information. However, it is challenging to explore and interpret activity space data, which often involve qualitative and quantitative variables describing both the environment and people's travel-activities. This paper will present a prototype software environment to visualize qualitative and quantitative urban activity space data. A system is developed to visualize the data generated by the Ecological Interview, a method for collecting activity space data, social network data, and perception of place data. This application integrates data on adolescents' travel behaviors, as well as their individual, social and community resources, by mapping subjects' activity spaces using multiple visual attributes (e.g. symbolization, color). Users can investigate the activity space data through an interactive interface developed using a commercial GIS software package. The case studies show how the visualization assists in the exploratory studies of activity space data. The visualization can also be used in post-modeling analysis through in-depth investigation of the multiple attributes for the best-fit and worst-fit cases. / Geography

We propose a methodology for calibrating the activity space and the cameras involved in video capture systems for upper extremity stroke rehabilitation. We discuss an in-home stroke rehabilitation system called Semi-Automated Rehabilitation At Home System (SARAH) and a clinic-based system called Action Research Arm Test (ARAT) developed by the Interactive Neuro-Rehabilitation Lab (INR) at Virginia Tech. We propose a calibration workflow for achieving invariant video capture across multiple therapy sessions. This ensures that the captured data is less noisy. In addition, there is prior knowledge of the captured activity space and patient location in the video frames provided to the Computer Vision algorithms analyzing the captured data. Such a standardized calibration approach improved machine learning analysis of patient movements and a higher rate of agreement across multiple therapists regarding the captured patient performance. We further propose a Multi-Camera Calibration approach to perform stereo camera calibration in SARAH and ARAT capture systems to help perform a 3D reconstruction of the activity space from 2D videos. The importance of the proposed activity space and camera calibration workflows, including new research paths opened as a result of our approach, are discussed in this thesis. / Master of Science / In this thesis, I describe the workflows I developed to perform calibration of stroke rehabilitation activity spaces, including the calibration of cameras involved in video capture systems for analyzing patient movements in stroke rehabilitation practices. The proposed workflows are designed to facilitate convenient user involvement in calibrating the video capture systems to provide invariant and consistent video captures, including the extraction of fine-grain information utilizing camera calibration results, to the therapists and computer vision-based automated systems for improved analysis of patient performance in stroke rehabilitation practices. The importance of human-in-the-loop systems, including future research paths to strengthen the symbiotic relationship between humans and Artificial Intelligence systems in stroke rehabilitation practices, is discussed. The quantitative and qualitative results generated from the workshops conducted to test and evaluate the calibration workflows align with the stakeholder's needs in stroke rehabilitation systems.

Macroscopic changes in the urban environment and in the built transportation infrastructure, as well as changes in household demographics and socio-economics, can lead to spatio-temporal variations in household travel patterns and therefore regional travel demand. Dynamics in travel behavior may also simply arise from the randomness associated with values, perceptions, attitudes, needs, preferences and decision-making process of the individual travelers. Most urban travel behavior models and analysis seek to explain variations in travel behavior in terms of characteristics of the individuals and their environment. Spatial extents and temporal variation in an individual’s travel pattern may represent a measure of the individual’s spatial appetite for activity and the variability-seeking nature on his/her travel behavior. The objective of this dissertation effort is to develop a methodology to predict activity participation using revealed spatial extents and temporal variability as variables that represent the spatial appetite and variability-seeking nature associated with individual household. Activity participation is defined as a set of activities in which an individual or household takes part, to satisfy the sustenance, maintenance and discretionary needs of the household. To accomplish the goals of the dissertation, longitudinal travel data collected from the Commute Atlanta Study are used. The raw Global Positioning Systems (GPS) data are processed to summarize trip data by household travel day and individual travel day data. A methodology was developed to automatically identify the activity at the end of each trip. Methods were then developed to estimate travel behavior variability that can represent the variability-seeking nature of the individual. Existing methods to estimate activity space were reviewed and a new Modified Kernel Density area method was developed to address issues with current methods. Finally activity participation models using structural equation modeling methods were developed and the effects of the variability-seeking nature and spatial extent of activities were applied to the models. The variability-seeking nature was presented in the activity participation model as a latent variable with coefficient of variation of trips and distance as indicator variables. The dissertation research found that inclusion of activity space variables can improve the activity participation modeling process to better explain travel behavior.

This master thesis deals with the question, whether the town Říčany performs functions of the local centre for its residents. Part of the text is devoted to the transformation taking place around the Czech capital. The paper uses the theoretical framework of Time geography. Using a qualitative questionnaire survey targeted at families with children, the work is trying to identify, where the residents localize their activities. The thesis also attempts to recognize the activity localisation mechanisms. Another goal was to find differences between various categories of the respondents. Children and adults working in Říčany usually localize their activities close to their home. Adults working in Prague have an elliptic activity space, in which home is however main station. One of the contributions of this work is the typology of four groups of households. In addition to long-term residents and two types of suburbanites, the group of "grounded residents" was defined. Although they have moved to the town rather recently, they usually live in the older parts of the town and localize there most of their life.

Buruli ulcer (BU), an infectious disease caused by Mycobacterium ulcerans, is the third most common mycobacterial disease after leprosy and tuberculosis and a WHO-defined neglected tropical disease. Despite years of research, the mode of transmission of BU remains unknown. This master’s thesis provides an integrated spatial analysis of disease dynamics in Ghana, West Africa, an area of comparatively high BU incidence. Within a case/matched control study design, environmental factors associated with BU infection and spatial behaviors are investigated to uncover possible links between individual daily activity spaces and terrains of risk across disturbed landscapes. This research relies upon archival and field-collected data and analyses conducted with geographical information systems (GIS).

Mental map is considered as an individual’s mental representation of his/her spatial cognition. People learn from the environment and add information to their personal mental map. It becomes important when we try to understand the relationships between one’s travel decision processes and their choice sets. The aim of this paper is to study the relationship between individuals’ activity travel patterns and their mental map by exploring people’s spatial cognition, their activity space and related factors. Two-week travel diary and mental maps were collected for the same 57 individuals in Stockholm. Respondents were asked to report their recent trip information in the travel diary and draw their familiar areas in specified maps. The specified maps, to some extent, reflect respondents’ mental maps by transferring this abstract concept from one’s mind to a visual representation. The derived mental maps were manually drawn and transferred from graph to ASCII code in ArcGIS. The visited activity locations on where people travelled during the observed period were used to construct one’s activity space. The key determinants that construct these activity space and mental map will be investigated. Marginal effect of each key variable will be calculated to understand the magnitude of influence of each variable into the spatial distribution of the given individual’s activity space and mental map. The result shows that individual’s activity space is not necessarily within individual mental map. Both activity space and mental map are correlated with individual’s travel pattern factors. Mental map has positive influence to the formation of activity space. The inference of marginal effect is useful for urban planning, promoting transport policies and analyzing the effect of transport infrastructure since it can help to locate the places that constitute individual’s activity space and mental map areas.

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Epidemic obesity in the U.S. has prompted exploration of causal factors related to the built environment. Recent research has noted statistical associations between the spatial accessibility of retail food sources, such as supermarkets, convenience stores, and restaurants, and individual characteristics such as weight, socioeconomic status, and race/ethnicity. These studies typically use residential proximity or neighborhood density to food sources as the measure of accessibility. Assessing food environments in this manner, however, is very limiting. Since most people travel outside of their neighborhood on a daily basis, the retail food sources available to individuals residing in the same area could vary widely. This research developed new techniques for describing food accessibility or food environments based upon individuals’ activity and travel patterns, or their activity spaces. Researchers have previously used travel diaries to study activity and travel behavior, but these are burdensome for participants, and are prone to recall error and other inaccuracies. This study explored use of global positioning system (GPS) to identify participants' activity spaces, and employed a geographic information system (GIS) to assess the retail food sources located within these spaces. This produced ‘activity-based’ measures of individual retail food accessibility that do not rely on areal units, nor require travel diaries. Participants included 121 residents of a census tract in Lexington, Kentucky who agreed to carry GPS trackers for three workdays, and complete surveys regarding weight, socioeconomic and demographic characteristics, and diet and food purchasing habits. The types and relative frequencies of food locations within their activity spaces were compared to those within close proximity to the census tract. Dietary and food purchasing habits were subsequently analyzed in relation to activity-based food environment measures. The results of this study demonstrate substantial potential for misclassification bias in food accessibility research based on residential proximity or neighborhood density. Furthermore, this study observed statistically significant relationships between the new activity-based food accessibility measures and some personal characteristics and food-related behaviors. Despite some limitations, the techniques developed in this research show great potential for future research, which should be explored further in a variety of contexts.