Prescriptive Analytics in Urban Policing Operations

2021 ◽  
Author(s):  
Tobias Brandt ◽  
Oliver Dlugosch ◽  
Ayman Abdelwahed ◽  
Pieter L. van den Berg ◽  
Dirk Neumann
Keyword(s):  
Response Time ◽  
Smart City ◽  
Problem Definition ◽  
Data Driven ◽  
Single Type ◽  
Prediction Methods ◽  
Trade Offs ◽  
Prescriptive Analytics ◽  
Crime Types ◽  
The Impact

Problem definition: We consider the case of prescriptive policing, that is, the data-driven assignment of police cars to different areas of a city. We analyze key problems with respect to prediction, optimization, and evaluation as well as trade-offs between different quality measures and crime types. Academic/practical relevance: Data-driven prescriptive analytics is gaining substantial attention in operations management research, and effective policing is at the core of the operations of almost every city in the world. Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context. Methodology: We conduct a computational study using crime and auxiliary data on the city of San Francisco. We analyze both strong and weak prediction methods along with two optimization formulations representing the deterrence and response time impact of police vehicle allocations. We analyze trade-offs between these effects and between different crime types. Results: We find that even weaker prediction methods can produce Pareto-efficient outcomes with respect to deterrence and response time. We identify three different archetypes of combinations of prediction methods and optimization objectives that constitute the Pareto frontier among the configurations we analyze. Furthermore, optimizing for multiple crime types biases allocations in a way that generally decreases single-type performance along one outcome metric but can improve it along the other. Managerial implications: Although optimization integrating all relevant crime types is theoretically possible, it is practically challenging because each crime type requires a collectively consistent weight. We present a framework combining prediction and optimization for a subset of key crime types with exploring the impact on the remaining types to support implementation of operations-focused smart city solutions in practice.

scholarly journals We Are on the Way: Analysis of On-Demand Ride-Hailing Systems

2020 ◽  
Author(s):  
Guiyun Feng ◽  
Guangwen Kong ◽  
Zizhuo Wang
Keyword(s):  
Waiting Time ◽  
Heuristic Method ◽  
Problem Definition ◽  
Lower Efficiency ◽  
Average Waiting Time ◽  
On Demand ◽  
Trade Offs ◽  
Matching Mechanism ◽  
Service Paradigm ◽  
The Impact

Problem definition: Recently, there has been a rapid rise of on-demand ride-hailing platforms, such as Uber and Didi, which allow passengers with smartphones to submit trip requests and match them to drivers based on their locations and drivers’ availability. This increased demand has raised questions about how such a new matching mechanism will affect the efficiency of the transportation system—in particular, whether it will help reduce passengers’ average waiting time compared with traditional street-hailing systems. Academic/practical relevance: The on-demand ride-hailing problem has gained much academic interest recently. The results we find in the ride-hailing system have a significant deviation from classic queueing theory where en route time does not play a role. Methodology: In this paper, we shed light on this question by building a stylized model of a circular road and comparing the average waiting times of passengers under various matching mechanisms. Results: We discover the inefficiency in the on-demand ride-hailing system when the en route time is long, which may result in nonmonotonicity of passengers’ average waiting time as the passenger arrival rate increases. After identifying key trade-offs between different mechanisms, we find that the on-demand matching mechanism could result in lower efficiency than the traditional street-hailing mechanism when the system utilization level is medium and the road length is long. Managerial implications: To overcome the disadvantage of both systems, we further propose adding response caps to the on-demand ride-hailing mechanism and develop a heuristic method to calculate a near-optimal cap. We also examine the impact of passenger abandonments, idle time strategies of taxis, and traffic congestion on the performance of the ride-hailing systems. The results of this research would be instrumental for understanding the trade-offs of the new service paradigm and thus enable policy makers to make more informed decisions when enacting regulations for this emerging service paradigm.

System-level Trade-offs and Optimization for Data-Driven Applications

2018 ◽  
Vol 2018 (1) ◽  
pp. 000023-000028
Author(s):  
Pushkar Apte ◽  
Tom Salmon ◽  
Richard Rice ◽  
Mark Gerber ◽  
Patricia Macleod ◽  
...  
Keyword(s):  
Technology Development ◽  
Data Driven ◽  
System Level ◽  
Performance Limits ◽  
Early Assessment ◽  
Trade Offs ◽  
2D System ◽  
Collaborative Platform ◽  
On Chip ◽  
The Impact

Abstract Data-driven applications are becoming increasingly important, fueled by the rapid rise of the Internet of Things (IoT) and Artificial Intelligence (AI). Systems must now be able to store, process and act swiftly on increasingly large amounts of data, while consuming minimum possible power. This shifts the focus to system-level integration and optimization – especially as Moore's Law slows down, and technology development at 5nm and beyond becomes increasingly harder and more expensive. SEMI has built a cross-supply-chain collaborative platform specifically to enable an early assessment of trade-offs and future technologies (5–8 years out). The first project focused on interconnect strategies, which are critical to most computing systems. We examined the performance limits for the best possible options for on-chip interconnects at technology nodes <= 20 nm. These limits highlight the need for system-level strategies, and we studied these by comparing a two-dimensional (2D) system with an interposer-based system (2.5D) to quantify the impact of the latter on the energy-delay product for various applications, especially data-driven ones.

scholarly journals The impact of differing frames on early stages of intersectoral collaboration: the case of the First 1000 Days Initiative in the Western Cape Province

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Ida Okeyo ◽  
Uta Lehmann ◽  
Helen Schneider
Keyword(s):  
Problem Definition ◽  
Intersectoral Collaboration ◽  
Western Cape ◽  
Western Cape Province ◽  
Policy Problem ◽  
Documentary Analysis ◽  
Trade Offs ◽  
Problems And Solutions ◽  
Disciplinary Background ◽  
The Impact

Abstract Background While intersectoral collaboration is considered valuable and important for achieving health outcomes, there are few examples of successes. The literature on intersectoral collaboration suggests that success relies on a shared understanding of what can be achieved collectively and whether stakeholders can agree on mutual goals or acceptable trade-offs. When health systems are faced with negotiating intersectoral responses to complex issues, achieving consensus across sectors can be a challenging and uncertain process. Stakeholders may present divergent framings of the problem based on their disciplinary background, interests and institutional mandates. This raises an important question about how different frames of problems and solutions affect the potential to work across sectors during the initiating phases of the policy process. Methods In this paper, this question was addressed through an analysis of the case of the First 1000 Days (FTD) Initiative, an intersectoral approach targeting early childhood in the Western Cape Province of South Africa. We conducted a documentary analysis of 34 policy and other documents on FTD (spanning global, national and subnational spheres) using Schmidt’s conceptualisation of policy ideas in order to elicit framings of the policy problem and solutions. Results We identified three main frames, associated with different sectoral positionings — a biomedical frame, a nurturing care frame and a socioeconomic frame. Anchored in these different frames, ideas of the problem (definition) and appropriate policy solutions engaged with FTD and the task of intersectoral collaboration at different levels, with a variety of (sometimes cross) purposes. Conclusions The paper concludes on the importance of principled engagement processes at the beginning of collaborative processes to ensure that different framings are revealed, reflected upon and negotiated in order to arrive at a joint determination of common goals.

scholarly journals Carbon Dynamics in the Northeastern Qinghai–Tibetan Plateau from 1990 to 2030 Using Landsat Land Use/Cover Change Data

2020 ◽  
Vol 12 (3) ◽  
pp. 528 ◽  
Author(s):  
Jingye Li ◽  
Jian Gong ◽  
Jean-Michel Guldmann ◽  
Shicheng Li ◽  
Jie Zhu
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Carbon Storage ◽  
Sharp Decrease ◽  
Total Carbon ◽  
Qinghai Lake ◽  
Lake Basin ◽  
Ecosystem Carbon ◽  
Trade Offs ◽  
The Impact

Land use/cover change (LUCC) has an important impact on the terrestrial carbon cycle. The spatial distribution of regional carbon reserves can provide the scientific basis for the management of ecosystem carbon storage and the formulation of ecological and environmental policies. This paper proposes a method combining the CA-based FLUS model and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model to assess the temporal and spatial changes in ecosystem carbon storage due to land-use changes over 1990–2015 in the Qinghai Lake Basin (QLB). Furthermore, future ecosystem carbon storage is simulated and evaluated over 2020–2030 under three scenarios of natural growth (NG), cropland protection (CP), and ecological protection (EP). The long-term spatial variations in carbon storage in the QLB are discussed. The results show that: (1) Carbon storage in the QLB decreased at first (1990–2000) and increased later (2000–2010), with total carbon storage increasing by 1.60 Tg C (Teragram: a unit of mass equal to 1012 g). From 2010 to 2015, carbon storage displayed a downward trend, with a sharp decrease in wetlands and croplands as the main cause; (2) Under the NG scenario, carbon reserves decrease by 0.69 Tg C over 2020–2030. These reserves increase significantly by 6.77 Tg C and 7.54 Tg C under the CP and EP scenarios, respectively, thus promoting the benign development of the regional ecological environment. This study improves our understanding on the impact of land-use change on carbon storage for the QLB in the northeastern Qinghai–Tibetan Plateau (QTP).

scholarly journals Computation and data driven discovery of topological phononic materials

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jiangxu Li ◽  
Jiaxi Liu ◽  
Stanley A. Baronett ◽  
Mingfeng Liu ◽  
Lei Wang ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Electronic System ◽  
Nodal Line ◽  
Data Driven ◽  
Single Type ◽  
Type I ◽  
Structure Property ◽  
Topological Quantum ◽  
Data Driven Approach ◽  
Spin Electronic

AbstractThe discovery of topological quantum states marks a new chapter in both condensed matter physics and materials sciences. By analogy to spin electronic system, topological concepts have been extended into phonons, boosting the birth of topological phononics (TPs). Here, we present a high-throughput screening and data-driven approach to compute and evaluate TPs among over 10,000 real materials. We have discovered 5014 TP materials and grouped them into two main classes of Weyl and nodal-line (ring) TPs. We have clarified the physical mechanism for the occurrence of single Weyl, high degenerate Weyl, individual nodal-line (ring), nodal-link, nodal-chain, and nodal-net TPs in various materials and their mutual correlations. Among the phononic systems, we have predicted the hourglass nodal net TPs in TeO3, as well as the clean and single type-I Weyl TPs between the acoustic and optical branches in half-Heusler LiCaAs. In addition, we found that different types of TPs can coexist in many materials (such as ScZn). Their potential applications and experimental detections have been discussed. This work substantially increases the amount of TP materials, which enables an in-depth investigation of their structure-property relations and opens new avenues for future device design related to TPs.

scholarly journals Networking for Cloud Robotics: The DewROS Platform and Its Application

2021 ◽  
Vol 10 (2) ◽  
pp. 34
Author(s):  
Alessio Botta ◽  
Jonathan Cacace ◽  
Riccardo De Vivo ◽  
Bruno Siciliano ◽  
Giorgio Ventre
Keyword(s):  
Response Time ◽  
Large Data ◽  
Cloud Services ◽  
Cloud Provider ◽  
Human Beings ◽  
Minimal Impact ◽  
Network Connection ◽  
Cloud Robotics ◽  
Networking Technologies ◽  
The Impact

With the advances in networking technologies, robots can use the almost unlimited resources of large data centers, overcoming the severe limitations imposed by onboard resources: this is the vision of Cloud Robotics. In this context, we present DewROS, a framework based on the Robot Operating System (ROS) which embodies the three-layer, Dew-Robotics architecture, where computation and storage can be distributed among the robot, the network devices close to it, and the Cloud. After presenting the design and implementation of DewROS, we show its application in a real use-case called SHERPA, which foresees a mixed ground and aerial robotic platform for search and rescue in an alpine environment. We used DewROS to analyze the video acquired by the drones in the Cloud and quickly spot signs of human beings in danger. We perform a wide experimental evaluation using different network technologies and Cloud services from Google and Amazon. We evaluated the impact of several variables on the performance of the system. Our results show that, for example, the video length has a minimal impact on the response time with respect to the video size. In addition, we show that the response time depends on the Round Trip Time (RTT) of the network connection when the video is already loaded into the Cloud provider side. Finally, we present a model of the annotation time that considers the RTT of the connection used to reach the Cloud, discussing results and insights into how to improve current Cloud Robotics applications.

scholarly journals Discrete choice experiment for eliciting preference for health services for patients with ALS and their informal caregivers

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Katy Tobin ◽  
Sinead Maguire ◽  
Bernie Corr ◽  
Charles Normand ◽  
Orla Hardiman ◽  
...  
Keyword(s):  
Health Services ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Strong Preference ◽  
Multidisciplinary Teams ◽  
Relative Importance ◽  
Trade Offs ◽  
Als Patients ◽  
The Impact

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative condition with a mean life expectancy of 3 years from first symptom. Understanding the factors that are important to both patients and their caregivers has the potential to enhance service delivery and engagement, and improve efficiency. The Discrete Choice Experiment (DCE) is a stated preferences method which asks service users to make trade-offs for various attributes of health services. This method is used to quantify preferences and shows the relative importance of the attributes in the experiment, to the service user. Methods A DCE with nine choice sets was developed to measure the preferences for health services of ALS patients and their caregivers and the relative importance of various aspects of care, such as timing of care, availability of services, and decision making. The DCE was presented to patients with ALS, and their caregivers, recruited from a national multidisciplinary clinic. A random effects probit model was applied to estimate the impact of each attribute on a participant’s choice. Results Patients demonstrated the strongest preferences about timing of receiving information about ALS. A strong preference was also placed on seeing the hospice care team later rather than early on in the illness. Patients also indicated their willingness to consider the use of communication devices. Grouping by stage of disease, patients who were in earlier stages of disease showed a strong preference for receipt of extensive information about ALS at the time of diagnosis. Caregivers showed a strong preference for engagement with healthcare professionals, an attribute that was not prioritised by patients. Conclusions The DCE method can be useful in uncovering priorities of patients and caregivers with ALS. Patients and caregivers have different priorities relating to health services and the provision of care in ALS, and patient preferences differ based on the stage and duration of their illness. Multidisciplinary teams must calibrate the delivery of care in the context of the differing expectations, needs and priorities of the patient/caregiver dyad.

scholarly journals Modeling Energy Communities with Collective Photovoltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 323
Author(s):  
Guilherme Pontes Luz ◽  
Rodrigo Amaro e Silva
Keyword(s):  
Economic Feasibility ◽  
Small City ◽  
Total System ◽  
Trade Offs ◽  
Southern Portugal ◽  
Energy Community ◽  
Individual Self ◽  
The Impact ◽  
The City ◽  
Modest Reduction

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 99 ◽  
Author(s):  
Yueqi Gu ◽  
Orhun Aydin ◽  
Jacqueline Sosa
Keyword(s):  
Los Angeles ◽  
A Priori ◽  
Safety Data ◽  
Spatial Optimization ◽  
Optimal Number ◽  
Multidisciplinary Optimization ◽  
Data Driven ◽  
Fault System ◽  
Design Data ◽  
The Impact

Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.

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