The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment

Artificial Intelligence and the Public Sector—Applications and Challenges

“…This category mainly covers studies that address AI applications with a focus on improving workflows, AI forecasting models, data management, as well as decision and knowledge management (e.g. Chou, Lin, Pham, & Shao, 2015;Chun & Wai, 2007Kouziokas, 2017;Kouziokas, Chatzigeorgiou, & Perakis, 2017;Metaxiotis, Ergazakis, Samouilidis, & Psarras, 2003;Sun & Medaglia, 2017;Zheng et al, 2018). Most important to our study is the approach of Zheng et al (2018) who investigate AI service provision by the government, highlighting the bilateral relationship between the needs of the public sector and the solutions provided by AI applications.…”

“…Moreover, the study by Kouziokas (2017) investigates public security issues related to AI, focusing on AI for risk prevention strategies in transportation management. By using computer-based artificial neural networks, the author connects security-related issues with the quality of transportation services to identify regions with a high crime rate (Kouziokas, 2017). As can be seen, AI in general has become a popular field of research and the impact of AI applications on daily life is rising.…”

“…• Control and performance monitoring in public areas for police departments to determine terror threats and crime hotspots for preventive action (Power 2016) • Determine high crime-risk situations to secure public transport (Kouziokas 2017) • Forecast model to predict water levels ( Against this background, opportunities arise for public officials as they may now focus on specified tasks and activities, while machines perform routine procedures. At the same time, however, the threat of human replacement persists, thus raising ethical concerns in connection with the use of AI technology in a public context.…”

Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management

“…• combining machine learning and cellular automata for simulating land use changes [47] and urban growth [48]; as mentioned in the previous subsection of this paper, the potential of integrating AI with CA has been being explored in recent years and is proving to be highly promising; • integrating an ML-CA model (MachCA) with nonlinear transition rules based on least squares support vector machines (LS-SVM) to simulate urban growth [49]; with the case study of Shanghai Qingpu-Songjiang area in China, authors demonstrated that the spatial configurations of rural-urban patterns can be modeled with application of the MachCA for simulating urban growth; • integrating artificial immune systems with CA for simulating land-use dynamics under planning policies [50]; authors tested their model on the case study of the Pearl River Delta in southern China and proved that their model could be useful in exploring various planning scenarios of urban development; • traffic flow prediction [51]; according to the authors, it has been "the first time that a deep architecture model was applied using auto-encoders as building blocks to represent traffic flow features for prediction"; additionally, the proposed method for traffic flow prediction demonstrated very good performance; • using artificial neural networks to forecast high crime risk transportation areas in urban environment [52]; the author offered a combination of spatial clustering methods and artificial neural network models in order to predict the high crime risk transportation areas, and consequently, to improve the quality of the transportation services and also to ensure public transportation safety • integrating knowledge-based systems with artificial neural networks and fuzzy systems to automate decision-making processes in urban planning [53]; authors indicated that combining these methods for developing urban development alternatives "achieves improvements in the implementation of each respective system as well as an increase in the breadth of functionality within the application"; • using machine learning for classifying residential areas on the basis of spatial patterns detected in a database of point locations of structures [54]; the authors tested their method in seven provinces of Afghanistan and demonstrated how to accurately map land uses and distinguish residential settlement types with 78% to 90% classification accuracy; • using machine learning for modeling complex socio-spatial processes such as gentrification [55]; the authors used the case study of London neighborhoods and showed that machine learning can be useful to "analyze existing patterns and processes of neighborhood change to identify areas likely to experience change in the future"; additionally, they stress that "qualitative case studies must be confronted with-and complemented by-predictions stemming from other, more extensive approaches"; • using catboats in areas of government that deal with customer service.…”

A Closed Loop Inverse Kinematics Solver Intended for Offline Calculation Optimized with GA

“…Artificial Intelligence is a hot topic of research, with a vast area of applications [21][22][23][24][25]. Genetic Algorithms (GA) is a metaheuristic which belongs to the larger class Evolutionary Algorithms (EA).…”