scholarly journals Simulating Offender Mobility: Modeling Activity Nodes from Large-Scale Human Activity Data

2020 ◽  
Vol 68 ◽  
pp. 541-570
Author(s):  
Raquel Rosés ◽  
Cristina Kadar ◽  
Charlotte Gerritsen ◽  
Chris Rouly
Keyword(s):  
Human Activity ◽  
Urban Areas ◽  
Large Scale ◽  
Complex Dynamics ◽  
Open Data ◽  
Human Movement ◽  
Urban Structure ◽  
Real World Data ◽  
Activity Data ◽  
Agent Based

In recent years, simulation techniques have been applied to investigate the spatiotemporal dynamics of crime. Researchers have instantiated mobile offenders in agent-based simulations for theory testing, experimenting with crime prevention strategies, and exploring crime prediction techniques, despite facing challenges due to the complex dynamics of crime and the lack of detailed information about offender mobility. This paper presents a simulation model to explore offender mobility, focusing on the interplay between the agent's awareness space and activity nodes. The simulation generates patterns of individual mobility aiming to cumulatively match crime patterns. To instantiate a realistic urban environment, we use open data to simulate the urban structure, location-based social networks data to represent activity nodes as a proxy for human activity, and taxi trip data as a proxy for human movement between regions of the city. We analyze and systematically compare 35 different mobility strategies and demonstrate the benefits of using large-scale human activity data to simulate offender mobility. The strategies combining taxi trip data or historic crime data with popular activity nodes perform best compared to other strategies, especially for robbery. Our approach provides a basis for building agent-based crime simulations that infer offender mobility in urban areas from real-world data.

scholarly journals Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

2019 ◽  
Vol 11 (12) ◽  
pp. 1470 ◽  
Author(s):  
Nan Xia ◽  
Liang Cheng ◽  
ManChun Li
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Urban Areas ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Human Life ◽  
Human Movement ◽  
Night Time ◽  
Urban Mapping ◽  
The City

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

scholarly journals Exploring Algorithms for Revealing Freight Vehicle Tours, Tour-Types, and Tour-Chain-Types from GPS Vehicle Traces and Stop Activity Data

2019 ◽  
Vol 1 (2-3) ◽  
pp. 175-190 ◽  
Author(s):  
André Romano Alho ◽  
Takanori Sakai ◽  
Ming Hong Chua ◽  
Kyungsoo Jeong ◽  
Peiyu Jing ◽  
...  
Keyword(s):  
Large Scale ◽  
Essential Elements ◽  
Activity Data ◽  
Agent Based ◽  
Urban Freight ◽  
Assignment Algorithm ◽  
Payload Capacity ◽  
Vehicle Demand ◽  
Gps Traces ◽  
Chain Type

Abstract Freight vehicle tours and tour-chains are essential elements of state-the-art agent-based urban freight simulations as well as key units to analyse freight vehicle demand. GPS traces are typically used to extract vehicle tours and tour-chains and became available in a large scale to, for example, fleet management firms. While methods to process this data with the objective of analysing and modelling tour-based freight vehicle operations have been proposed, they were not fully explored with regard to the implication of underlying assumptions. In this context, we test different algorithms of stop-to-tour assignment, tour-type and tour-chain identification, aiming to expose their implications. Specifically, we compare the traditional stop-to-tour assignment algorithm using the location of a “base” as the start/end point of tours, against other algorithms using stop activities or payload capacity usage. Furthermore, we explore high-resolution tour-type/chain identification algorithms, considering stop types and recurrence of visits. For tour-chain identification, we explore two algorithms: one defines the day-level tour-chain-type based on the predominant tour-type identified for the period of 1 day and another defines the tour-chain-type based on the average number of stops per tour by stop type. For a demonstration purpose, we apply the methods to data from a large-scale GPS-based survey conducted during 2017–2019 in Singapore. We compare the algorithms in an assessment of freight vehicle operations day-to-day pattern homogeneity. Our analysis demonstrates that the predictions of tours, tourtypes, and tour-chain-types are highly dependent on the assumptions used, underlining the importance of carefully selecting and disclosing the methods for data processing. Finally, the exploration of day-to-day pattern homogeneity reveals operational differences across vehicle types and industries.

scholarly journals Intermodal Nodes for the European Metropolis: Amsterdam Zuidas as EURandstad's Gate

2014 ◽  
Vol 11 ◽  
pp. 220-226
Author(s):  
Manuela Triggianese ◽  
Fabrizia Berlingieri
Keyword(s):  
The Netherlands ◽  
Urban Areas ◽  
Urban Policy ◽  
Large Scale ◽  
Spatial Relations ◽  
Stable Configuration ◽  
Urban Structure ◽  
Design Strategies ◽  
South Axis ◽  
The City

Since more than fifty years, in the Netherlands, the Randstad Holland [1,2] represents a model of reference within the international debate on the sustainable balance between urban areas, infrastructural development and preservation of natural environment. The polycentric urban structure of the country progressively built up a new metropolitan reality of Europe, based on a stable configuration of cities’ spatial relations around the maintenance of the Green Hearth core and on strategic logics of infrastructural developments. However today the double awareness to rebalance growing population of urban areas and to open the region towards North-Central Europe, create fundamental conditions for a renewed expanding vision [3]. The current Dutch metropolitan perspective looks at the densest cities of Amsterdam and Rotterdam as main European and international gates, addressing large scale ambitions to clusters of urban developments at the intersection of main roads, railways and local infrastructures. This paper presents an investigative approach and intends to provoke academic discussion on the conflicting and possible relationships between urban policies and design strategies in the construction of a new metropolitan European perspective. Particular emphasis is put on the coordination between contemporary policies with spatial implications in the city of Amsterdam. Exploring its geographical advantages, the City tries to give form to policies’ abstraction of Randstad 2040 vision in the recent structural spatial Agenda, focused on strategic urban and economic cores. The current vision represents the metropolitan ambition of the Netherlands, where the project of Zuidas - literally South Axis - is a prime example of a new model of intermodal urban hub. Throughout the Dutch example, this paper attempts to presentZuidas testing its capability to enhance an innovative approach – in urban policy and spatial implication- to sustainable development.

scholarly journals Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

2019 ◽  
Vol 11 (4) ◽  
pp. 92 ◽  
Author(s):  
Jürgen Hackl ◽  
Thibaut Dubernet
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Urban Setting ◽  
Disease Spread ◽  
Model Parameters ◽  
Epidemic Spreading ◽  
Agent Based ◽  
Spread Model ◽  
Spread Dynamics

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

scholarly journals Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

2021 ◽  
Vol 10 (3) ◽  
pp. 165
Author(s):  
Joerg Schweizer ◽  
Cristian Poliziani ◽  
Federico Rupi ◽  
Davide Morgano ◽  
Mattia Magi
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Choice Model ◽  
Demand Model ◽  
Agent Based ◽  
Traffic Lights ◽  
Gps Traces ◽  
The City

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

Engineering in the time of cholera: overcoming institutional and political challenges to rebuild Zimbabwe's water and sanitation infrastructure in the aftermath of the 2008 cholera epidemic

2013 ◽  
Vol 3 (2) ◽  
pp. 222-229 ◽  
Author(s):  
Clarissa Brocklehurst ◽  
Murtaza Malik ◽  
Kiwe Sebunya ◽  
Peter Salama
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Humanitarian Aid ◽  
Urban Systems ◽  
Political Crisis ◽  
Rapid Improvement ◽  
Cholera Epidemic ◽  
Rapid Action ◽  
Water And Sanitation Infrastructure ◽  
Urban Rehabilitation

A devastating cholera epidemic swept Zimbabwe in 2008, causing over 90,000 cases, and leaving more than 4,000 dead. The epidemic raged predominantly in urban areas, and the cause could be traced to the slow deterioration of Zimbabwe's water and sewerage utilities during the economic and political crisis that had gripped the country since the late 1990s. Rapid improvement was needed if the country was to avoid another cholera outbreak. In this context, donors, development agencies and government departments joined forces to work in a unique partnership, and to implement a programme of swift improvements that went beyond emergency humanitarian aid but did not require the time or massive investment associated with full-scale urban rehabilitation. The interventions ranged from supply of water treatment chemicals and sewer rods to advocacy and policy advice. The authors analyse the factors that made the programme effective and the challenges that partners faced. The case of Zimbabwe offers valuable lessons for other countries transitioning from emergency to development, and particularly those that need to take rapid action to upgrade failing urban systems. It illustrates that there is a ‘middle path’ between short-term humanitarian aid delivered in urban areas and large-scale urban rehabilitation, which can provide timely and highly effective results.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

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