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8. Why Topology Matters in Predicting Human Activities

Author

Ma, Ding, Omer, Itzhak, Osaragi, Toshihiro, Sandberg, Mats, and Jiang, Bin

Subjects
Physics - Physics and Society
Abstract

Geographic space is better understood through the topological relationship of the underlying streets (note: entire streets rather than street segments), which enables us to see scaling or fractal or living structure of far more less-connected streets than well-connected ones. It is this underlying scaling structure that makes human activities predictable, albeit in the sense of collective rather than individual human moving behavior. This topological analysis has not yet received its deserved attention in the literature, as many researchers continue to rely on segment analysis for predicting human activities. The segment-analysis-based methods are essentially geometric, with a focus on geometric details of locations, lengths, and directions, and are unable to reveal the scaling property, which means they cannot be used for human activities prediction. We conducted a series of case studies using London streets and tweet location data, based on related concepts such as natural streets, and natural street segments (or street segments for short), axial lines, and axial line segments (or line segments for short). We found that natural streets are the best representation in terms of human activities or traffic prediction, followed by axial lines, and that neither street segments nor line segments bear a good correlation between network parameters and tweet locations. These findings point to the fact that the reason why axial lines-based space syntax, or the kind of topological analysis in general, works has little to do with individual human travel behavior or ways that human conceptualize distances or spaces. Instead, it is the underlying scaling hierarchy of streets - numerous least-connected, a very few most-connected, and some in between the least- and most-connected - that makes human activities predictable., Comment: 14 pages, 5 figures, 4 tables, Environment and Planning B: Urban Analytics and City Science, 2018

Published
2018
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11. Sequential Patterns of Daily Human Activity Extracted from Person Trip Survey Data

Author

Wang, Weiying, Osaragi, Toshihiro, Tagashira, Maki, Angelidou, Margarita, Editorial Board Member, Farnaz Arefian, Fatemeh, Editorial Board Member, Batty, Michael, Editorial Board Member, Davoudi, Simin, Editorial Board Member, DeVerteuil, Geoffrey, Editorial Board Member, Jones, Paul, Editorial Board Member, Kirby, Andrew, Editorial Board Member, Kropf, Karl, Editorial Board Member, Lucas, Karen, Editorial Board Member, Maretto, Marco, Editorial Board Member, Modarres, Ali, Editorial Board Member, Neuhaus, Fabian, Editorial Board Member, Nijhuis, Steffen, Editorial Board Member, Aráujo de Oliveira, Vitor Manuel, Editorial Board Member, Silver, Christopher, Editorial Board Member, Strappa, Giuseppe, Editorial Board Member, Vojnovic, Igor, Editorial Board Member, Whitehand, Jeremy W. R., Editorial Board Member, Yamu, Claudia, Editorial Board Member, Geertman, S. C. M., editor, Pettit, Christopher, editor, Goodspeed, Robert, editor, and Staffans, Aija, editor

Published
2021
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12. How Various Natural Disasters Impact Urban Human Mobility Patterns: A Comparative Analysis Based on Geotagged Photos Taken in Tokyo

Author

Derdouri, Ahmed, Osaragi, Toshihiro, Angelidou, Margarita, Editorial Board Member, Farnaz Arefian, Fatemeh, Editorial Board Member, Batty, Michael, Editorial Board Member, Davoudi, Simin, Editorial Board Member, DeVerteuil, Geoffrey, Editorial Board Member, Jones, Paul, Editorial Board Member, Kirby, Andrew, Editorial Board Member, Kropf, Karl, Editorial Board Member, Lucas, Karen, Editorial Board Member, Maretto, Marco, Editorial Board Member, Modarres, Ali, Editorial Board Member, Neuhaus, Fabian, Editorial Board Member, Nijhuis, Steffen, Editorial Board Member, Aráujo de Oliveira, Vitor Manuel, Editorial Board Member, Silver, Christopher, Editorial Board Member, Strappa, Giuseppe, Editorial Board Member, Vojnovic, Igor, Editorial Board Member, Whitehand, Jeremy W. R., Editorial Board Member, Yamu, Claudia, Editorial Board Member, Geertman, S. C. M., editor, Pettit, Christopher, editor, Goodspeed, Robert, editor, and Staffans, Aija, editor

Published
2021
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13. Pedestrian Behavior Characteristics Based on an Activity Monitoring Survey in a University Campus Square

Author

Osaragi, Toshihiro, Yamada, Yuriko, Kaneko, Hiroyuki, Angelidou, Margarita, Editorial Board Member, Farnaz Arefian, Fatemeh, Editorial Board Member, Batty, Michael, Editorial Board Member, Davoudi, Simin, Editorial Board Member, DeVerteuil, Geoffrey, Editorial Board Member, Jones, Paul, Editorial Board Member, Kirby, Andrew, Editorial Board Member, Kropf, Karl, Editorial Board Member, Lucas, Karen, Editorial Board Member, Maretto, Marco, Editorial Board Member, Modarres, Ali, Editorial Board Member, Neuhaus, Fabian, Editorial Board Member, Nijhuis, Steffen, Editorial Board Member, Aráujo de Oliveira, Vitor Manuel, Editorial Board Member, Silver, Christopher, Editorial Board Member, Strappa, Giuseppe, Editorial Board Member, Vojnovic, Igor, Editorial Board Member, Whitehand, Jeremy W. R., Editorial Board Member, Yamu, Claudia, Editorial Board Member, Geertman, S. C. M., editor, Pettit, Christopher, editor, Goodspeed, Robert, editor, and Staffans, Aija, editor

Published
2021
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37. Contradiction Hidden in Values of Urban Raster Data.

Author

Osaragi, Toshihiro and Aoki, Yoshitsugu

Subjects
LAND use, CONTRADICTION, PROBABILITY theory
Abstract

This paper discusses that raster data identified by the independent largest fraction method may include both manifest and hidden contradictions, and describes the underlying mechanisms of contradictions. Using the examples of zones of land use and floor-area ratio, we demonstrate that the cells including more than three different zone category combinations might be identified with a non-existing combination of the zone categories. For the zones in which the ratio of contradictory cells is comparatively large, this problem can be quite significant. We model, therefore, the probability of contradictory identification using the adjacency relationship of different categories, and demonstrate the good fitness of the model using actual urban raster data. The results reveal that there can be a significant proportion of hidden contradictions. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Classification Method by Information Loss Minimization for Visualizing Spatial Data

Author

Osaragi, Toshihiro, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Borruso, Giuseppe, editor, Torre, Carmelo M., editor, Rocha, Ana Maria A.C., editor, Taniar, David, editor, Apduhan, Bernady O., editor, Stankova, Elena, editor, and Cuzzocrea, Alfredo, editor

Published
2017
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48. Spatiotemporal Interpolation Method for Population Flow Data in Urban Areas.

Author

Osaragi, Toshihiro, Nan, Xianshu, and Kishimoto, Maki

Subjects
CITIES & towns, RESIDENTIAL mobility, INTERPOLATION, DEMOGRAPHY, HAZARD mitigation, SQUARE root, POPULATION dynamics
Abstract

Capturing the intricate dynamics of population movements in urban areas holds substantial implications for urban planning and management, particularly in the context of disaster mitigation. There is an attempt to introduce methods for estimating the spatiotemporal distribution of population flows, leveraging demographic data from various kind of sources. In earlier spatiotemporal interpolation methods, some key assumptions were made to obtain data at shorter intervals. In this study, we present an alternative spatiotemporal interpolation method by loosening assumptions and increase its versatility and facilitates flexible application across various contexts and objects. This is achieved by estimating the square root of the movement probability matrix for longer time intervals. The efficacy of our approach is demonstrated through its application to actual data from the Tokyo 23 wards, allowing for the estimation of the spatiotemporal distribution of population flows across various time intervals. Our results not only affirm the accuracy of the estimates but also provide insights into the intricate population flows within the densely populated regions of the Tokyo 23 wards. Moreover, by estimating population data at shorter time intervals, we explore the characteristics of these flows, offering an understanding of the dynamics that shape urban demography. [ABSTRACT FROM AUTHOR]

Published
2024
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