scholarly journals Safe Trajectory Generation for Complex Urban Environments Using Spatio-Temporal Semantic Corridor

2019 ◽  
Vol 4 (3) ◽  
pp. 2997-3004 ◽  
Author(s):  
Wenchao Ding ◽  
Lu Zhang ◽  
Jing Chen ◽  
Shaojie Shen
Keyword(s):  
Trajectory Generation ◽  
Urban Environments ◽  
Spatio Temporal

scholarly journals Prediction limits of mobile phone activity modelling

2017 ◽  
Vol 4 (2) ◽  
pp. 160900 ◽  
Author(s):  
Dániel Kondor ◽  
Sebastian Grauwin ◽  
Zsófia Kallus ◽  
István Gódor ◽  
Stanislav Sobolevsky ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Spatial Scales ◽  
Urban Environments ◽  
Full Spectrum ◽  
Left Behind ◽  
Social Events ◽  
Prediction Limits ◽  
Spatio Temporal ◽  
City Structure ◽  
The City

Thanks to their widespread usage, mobile devices have become one of the main sensors of human behaviour and digital traces left behind can be used as a proxy to study urban environments. Exploring the nature of the spatio-temporal patterns of mobile phone activity could thus be a crucial step towards understanding the full spectrum of human activities. Using 10 months of mobile phone records from Greater London resolved in both space and time, we investigate the regularity of human telecommunication activity on urban scales. We evaluate several options for decomposing activity timelines into typical and residual patterns, accounting for the strong periodic and seasonal components. We carry out our analysis on various spatial scales, showing that regularity increases as we look at aggregated activity in larger spatial units with more activity in them. We examine the statistical properties of the residuals and show that it can be explained by noise and specific outliers. Also, we look at sources of deviations from the general trends, which we find to be explainable based on knowledge of the city structure and places of attractions. We show examples how some of the outliers can be related to external factors such as specific social events.

Effective spatio-temporal semantic trajectory generation for similar pattern group identification

2019 ◽  
Vol 11 (2) ◽  
pp. 287-300 ◽  
Author(s):  
Yang Cao ◽  
Fei Xue ◽  
Yuanying Chi ◽  
Zhiming Ding ◽  
Limin Guo ◽  
...  
Keyword(s):  
Similar Pattern ◽  
Group Identification ◽  
Trajectory Generation ◽  
Spatio Temporal ◽  
Pattern Group

scholarly journals To fear or not to fear? Exploring the temporality of topophobia in urban environments

2020 ◽  
Vol 28 (4) ◽  
pp. 308-321
Author(s):  
Petr Šimáček ◽  
Miloslav Šerý ◽  
David Fiedor ◽  
Lucia Brisudová
Keyword(s):  
Urban Areas ◽  
Fear Of Crime ◽  
Urban Environments ◽  
Temporal Dimension ◽  
Empirical Survey ◽  
Mental Mapping ◽  
Local Decision ◽  
Spatio Temporal ◽  
Using Data ◽  
Over Time

AbstractThe concept of topophobia has been known in Geography for decades. Places which evoke fear in people’s minds can be found in almost every city. The perception of fear within an urban environment shows a certain spatio-temporal concentration and is often represented by fear of crime. The meaning of topophobic places, however, derived from the experience of fear of crime changes over time, and thus can alter the usual patterns of population behaviours in relation to time (in the time of the day and over longer periods) and space. A spatiotemporal understanding of these changes is therefore crucial for local decision-makers. Using data from the Czech Republic, this paper deals with the analysis of topophobic places, and is based on an empirical survey of the inhabitants of four cities, using the concept of mental mapping. In contrast to most similar geographical studies, the paper emphasises the temporal dimension of the fear of crime. The results have shown that over time there are significant differences in the meanings of topophobic places, and they have demonstrated the necessity of taking local specifics into account. The paper shows how the intensity of and the reasons for fears vary, depending on time and place. In general, the results provide support for the idea of place as a process and contain useful information for spatial planning and policy in urban areas.

scholarly journals ANALYSIS OF THE FLOATING CAR DATA OF TURIN PUBLIC TRANSPORTATION SYSTEM: FIRST RESULTS

2018 ◽  
Vol XLII-4 ◽  
pp. 515-521
Author(s):  
R. Ravanelli ◽  
M. Crespi
Keyword(s):  
Public Transportation ◽  
Low Cost ◽  
Temporal Analysis ◽  
Satellite System ◽  
Transportation System ◽  
Urban Environments ◽  
Floating Car Data ◽  
Public Transportation System ◽  
Spatio Temporal ◽  
Global Navigation Satellite

<p><strong>Abstract.</strong> Global Navigation Satellite System (GNSS) sensors represent nowadays a mature technology, low-cost and efficient, to collect large spatio-temporal datasets (Geo Big Data) of vehicle movements in urban environments. Anyway, to extract the mobility information from such Floating Car Data (FCD), specific analysis methodologies are required. In this work, the first attempts to analyse the FCD of the Turin Public Transportation system are presented. Specifically, a preliminary methodology was implemented, in view of an automatic and possible real-time impedance map generation. The FCD acquired by all the vehicles of the Gruppo Torinese Trasporti (GTT) company in the month of April 2017 were thus processed to compute their velocities and a visualization approach based on Osmnx library was adopted. Furthermore, a preliminary temporal analysis was carried out, showing higher velocities in weekend days and not peak hours, as could be expected. Finally, a method to assign the velocities to the line network topology was developed and some tests carried out.</p>

scholarly journals Spatial sensitivity analysis for urban hotspots using cell phone traces

2021 ◽  
pp. 239980832098584
Author(s):  
Jiahui Wu ◽  
Enrique Frias-Martinez ◽  
Vanessa Frias-Martinez
Keyword(s):  
Sensitivity Analysis ◽  
Cell Phone ◽  
Large Scale ◽  
Urban Environments ◽  
Urban Life ◽  
Spatial Sensitivity ◽  
Methodological Choices ◽  
Spatial Sensitivity Analysis ◽  
Spatio Temporal ◽  
The Impact

Urban hotspots can be used to model the structure of urban environments and to study or predict various aspects of urban life. An increasing interest in the analysis of urban hotspots has been triggered by the emergence of pervasive technologies that produce massive amounts of spatio-temporal data including cell phone traces (or Call Detail Records). Although hotspot analyses using cell phone traces are extensive, there is no consensus among researchers about the process followed to compute them in terms of four important methodological choices: city boundaries, spatial units, interpolation methods, and hotspot variables. Using a large-scale CDR dataset from Mexico, we provide an interpretable systematic spatial sensitivity analysis of the impact that these methodological choices might have on the stability of the hotspot variables in both static and dynamic settings.

scholarly journals Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4717
Author(s):  
Yacine Mohamed Idir ◽  
Olivier Orfila ◽  
Vincent Judalet ◽  
Benoit Sagot ◽  
Patrice Chatellier
Keyword(s):  
Air Quality ◽  
Mean Squared Error ◽  
Mobile Network ◽  
Urban Environments ◽  
Environmental Sensors ◽  
Simple Kriging ◽  
Distance Weighting ◽  
Geostatistical Models ◽  
Spatio Temporal ◽  
Inverse Distance

With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

Sign in / Sign up

Export Citation Format

Share Document