Environment and Planning B: Urban Analytics and City Science

This paper investigates the spatial distribution of social activity locations. The research makes use of a social interaction potential (SIP) metric to estimate the potential for an individual to participate in a face-to-face social activity at any particular location in the city. The metric is shown to constitute a contact probability field that is sensitive to time-geographic constraints such as home locations, workplaces, and travel times. Empirical case studies drawn from samples in Ghent, Belgium and Concepción, Chile are used to evaluate the effectiveness of the SIP metric in assigning high potential scores to observed social activity episodes. Moreover, a regression model is used to estimate the marginal benefit of using successive levels of constraint detail. The results illustrate both positive and negative aspects of the SIP metric. The metric behaves very well in general; 75% of the time an observed activity location received a score in the 25th percentile. However, lower valued scores were more common in cases when the time-geographic constraints were not very strong (ie, when the commute duration was short), or when activities took place in the homes of the respondents. In the end, the results are a step towards validating the regional scale SIP metric and indicate that it may be useful in microsimulation models of daily travel and activity participation.

In this paper we present a prism-based and gap-based approach to model shopping location choice. Location choice is a fundamental decision in the activity scheduling process. We propose a simple yet robust model to capture shopping location choice behaviour. In this model, an individual first chooses a time window (or gap); the choice of the shopping location depends on the gap chosen. This notion arises from our understanding that shopping location choice behaviour depends on shopping type, scheduling constraints, time of day, and day of week. Or quite simply, where you shop depends on when you shop. The gap-based approach to destination choice is envisioned as a small but significant step towards a more comprehensive location choice model in a dynamic scheduling environment.

Time-geographic density estimation (TGDE) provides a method for generating probability density surfaces for mobile objects. The technique operates by fitting distance-weighted geo-ellipses to each pair of points in a tracking dataset and combining them to create a final density surface. The sizes of the geo-ellipses, and thus the amount of smoothing, are determined by a velocity parameter which specifies how fast the object can travel. In prior formulations of TGDE this maximum-velocity parameter was treated as constant or fixed. This can be problematic if an object displays variable movement patterns, such as alternating between movements and stops. In this research we develop a more robust formulation of time-geographic density estimation, termed adaptive-velocity TGDE, which allows the maximum-velocity parameter to vary for each segment of the space-time path. First, the new mathematical formulation is demonstrated and compared with fixed-TGDE through an application to synthetic pedestrian tracking data. Second, both methods are evaluated in the context of accurately mapping the movement patterns of a homing pigeon tracked by GPS. The results of both applications demonstrate that adaptive-velocity TGDE has the potential to more accurately delineate the trajectories of mobile objects than the fixed-velocity version. Implications of these results in the context of developing a probabilistic time geography for general mobile objects are also discussed.

The aim of this paper is to assess the impact of uncertain travel times as reflected in travel time variability on the outcomes of individuals' activity-travel scheduling decisions, assuming they are faced with fixed space-time constraints and apply the set of decision rules that they have developed over time by learning how to cope with uncertainty in their environment. Features of resulting activity-travel patterns are compared for different travel times. Results of the analyses indicate that uncertain travel times are reflected primarily in changes in the start and end time of activities and the corresponding duration of activities. There is also evidence that some activities are cancelled, suggesting that increased travel times may have made some activity agendas unfeasible.

This paper presents a dynamic discrete choice model of activity scheduling that features classic time-geography properties within a microeconomic framework. We present results that show how the model can produce accessibilities that form space-time prisms, while retaining the properties of traditional measures based on consumer surplus in the form of logsums. The main features of the model are that it handles time-space constraints, travel time uncertainty, and endogenous trip chaining in one consistent framework. The resulting accessibility respects the individual's time budget and fixed activities. The dynamic discrete choice framework makes possible estimation of behavioural parameters using well-known methods. Some of the remaining computational challenges are discussed. The final section provides some examples of the policy analysis possibilities provided by a model of this kind.

Previous literature on transportation and land use has focused on the effect of individual land-use variables, such as population and employment density, and on measures of transportation demand, such as vehicle kilometers traveled and mode split. In contrast, our work uses activity spaces, a relatively unexplored measure of travel dispersal, as a dependent variable and neighborhood clusters to capture the effect of land use on this variable. This paper is an extension of previous research that dealt with Montreal exclusively and similar methods are used to compare three cities (Montreal, Quebec City, and Sherbrooke) over multiple years (1998–2008). We control and tests for the possibility of residential location self-selection bias through simultaneous equation modeling. The main findings are that (i) activity spaces are clearly linked to land use (through neighborhood clusters), as well as to overall city size; (ii) activity spaces appear to be growing over time where employment centers are fixed; and (iii) exogeneity in explanatory variables cannot be rejected.

Using the SITRAMP dataset, which was collected in the Jakarta metropolitan area, Indonesia, over four consecutive days, this study examines day-to-day variability of individuals' activity spaces. The impact of individual heterogeneity and variability of transport network conditions on day-to-day variability of activity spaces is also investigated. Results show that individuals' activity spaces vary from day to day and between different individuals. The activity space of other household members was found to be the most significant factor influencing an individual's activity space. Against the common belief in developing countries that better traffic conditions make individuals travel farther, results show that higher road-network travel speed and better road surface conditions within the home zones actually encourage individuals to visit a more compact set of activity locations and/or visit fewer activity locations. Smoother road surface conditions and higher travel speeds within home zones also bring the centroid of activity locations closer to individuals' home locations. Furthermore, day-to-day variability analysis of individual activity spaces showed that weekday activity spaces are more compact than those at weekends. Moreover, it was found that students' activity spaces show most variability, while those of nonworkers have the lowest variability.

Local-climate changes due to urbanization are epitomiz ed by the urban heat island (UHI), which is characterized by temperature differences between urban and rural areas. The UHI is a critical factor for energy consumption and air quality, resulting in higher peak electricity demand in summer because of air conditioning, increased emissions of primary pollutants associated with power production, and increased generation of ozone. However, planners need a better understanding of the relationship between the UHI and land-use patterns in order to reduce the UHI and promote more sustainable urban development. This research develops statistical models of local surface temperatures, using Landsat-5 satellite remote-sensing data, whereby the temperature at any location and for any land use is modeled as a function of the pattern of land uses around this location. Normalized Difference Vegetation Index (NDVI) and area land-use variables are used as inputs to these models, which are estimated with data for the Columbus, Ohio, metropolitan area. The results confirm the effects of neighboring land uses on local temperatures. The applicability of these models for land-use planning is illustrated by simulating hypothetical land-use changes, and computing the resulting temperature effects. The results demonstrate that it is possible to reduce temperatures in residential and urban areas through judicious siting of green areas.

Research on the enabling factors of innovation has most often addressed either the social component of organizations or the spatial dimensions involved in the innovation process. Few studies have examined the link from spatial layout and social networks to innovation. Social networks play important roles in structuring communication, collaboration, access to knowledge, and knowledge transformation. These processes are both antecedent to and part of the innovation process. Spatial layout structures patterns of circulation, proximity, awareness of others, and encounter in an organization. These interrelationships become fundamental to the development of social networks, especially those networks critical to the innovation process. This research explored associations between innovation within three partner organizations and the organization's social and spatial structure. The organizations included: A nonprofit life sciences institute dedicated to translational research on cancer, the research laboratories of a multinational software corporation, and the quality control group of an automobile manufacturer. The study applied spatial analysis to map and characterize physical space in conjunction with survey data capturing social contacts among researchers at the three organizations. For one partner organization, we augmented these tools with location-tracking methods. It could be argued that sociometric surveys capture the ‘perceived’ social network. Social networks researchers have been very interested in assessing ‘real’ networks either as reliability checks on sociometric survey networks, or as stand-alone networks. Our use of an ultrawideband location system allowed us to assess networks in real time. In interpreting our results, we suggest that through exposure to moving others, locations with high metric choice may provide the opportunities for serendipitous encounters among individuals who may come from disparate parts of an organization. Whereas low mean distance to others may provide the enhanced connections necessary to mobilize the resources and attention to move innovative ideas forward. Results demonstrate the salience of both social and spatial dimensions in the processes of innovation. The research suggests two strong factors that appear to influence our results: the institutional context which characterizes or prioritizes certain innovation outcomes; the extent to which the physical facility design of organizations tends to concentrate or spatially distribute the research unit. Our findings indicate that relationships between salutary network positions and beneficial locales themselves derive from institutional contexts that shape the priorities, opportunities, goals and practices of discovery. We suggest that innovation is a process that occurs at the intersection of social and physical space, and moves toward a sociospatial science of design for innovation.

Museums often suffer from so-called ‘hypercongestion’, wherein the number of visitors exceeds the capacity of the physical space of the museum. This can potentially be detrimental to the quality of visitors' experiences, through disturbance by the behavior and presence of other visitors. Although this situation can be mitigated by managing visitors' flow between spaces, a detailed analysis of visitor movement is required to realize fully and apply a proper solution to the problem. In this paper we analyze visitors' sequential movements, the spatial layout, and the relationship between them in a large- scale art museum—The Louvre Museum—using anonymized data collected through noninvasive Bluetooth sensors. This enables us to unveil some features of visitor behavior and spatial impact that shed some light on the mechanisms of museum overcrowding. The analysis reveals that the visiting styles of short-stay and long-stay visitors are not as significantly different as one might expect. Both types of visitors tend to visit a similar number of key locations in the museum while the longer-stay visitors just tend to do so more time extensively. In addition, we reveal that some ways of exploring the museum appear frequently for both types of visitors, although long-stay visitors might be expected to diversify much more, given the greater time spent in the museum. We suggest that these similarities and dissimilarities make for an uneven distribution of the number of visitors in the museum space. The findings increase the understanding of the unknown behaviors of visitors, which is key to improving the museum's environment and visitor experience.