The availability of urban big data offers new opportunities for developing many aspects of urban living. This data availability showcases that it can help make informed decisions for the optimal usage of resources. At the same time, new technologies such as the Internet of Things, artificial intelligence, and machine learning can significantly contribute to this process, allowing researchers and planners to conduct more in-depth and accurate urban analyses.

As urban space is a dynamic system composed of human and commercial activity, flows of energy and matter, and their interactions, we can no longer analyze the urban environment as a static space built of structures and roads. At the same time, in recent years, one can observe an increasing amount of big data mining applications in urban studies and planning practices. Urban extensive data mining—i.e., extrapolating patterns and obtaining new knowledge from existing data sources—allows new types of data to improve system performance and take full advantage of its real-time nature. At the same time, these new insights can also be an advantage for urban planning analyses.

AI in Urban Planning

Various urban research scholars argue that big data analytics supported by AI-based tools promise benefits in terms of real-time prediction, adaptation, higher energy efficiency, higher quality of life, and accessibility. Data-driven technologies, such as artificial intelligence, suggest ways to establish a new generation of GIS systems, as they enable the building of frameworks connecting multiple data sources. AI-based tools are applied in studies which require accurate predictions with a high spatiotemporal resolution, such as urban traffic surveillance systems and real-time pedestrian flow analysis. Big data analytics using AI-based tools could allow regional perspectives to be modelled at the individual level, move from static total amounts to dynamic flows, and reflect the fine-grained scale of regional spatial changes. With the help of cellular automata and multi-agent systems, this approach was used for forecasting urban growth.

Types of AI-Based Tools Used in Urban Planning

• AI-based tools used in urban planning can be divided into the following four groups according to their application and properties:
• Artificial life—cellular automata, agent-based model, swarm intelligence;
• Intelligent stochastic simulation models—the most important of which are genetic algorithms and simulated annealing;
• Evolutionary computing and spatial DNA—the most important of which are artificial neural networks (convolutional and recurrent) and spatial DNA;
• Knowledge-based intelligent systems—fuzzy logic, expert systems, heuristics, andreasoning systems.

Impact of AI and Urban Big Data Analysis

Those analyses mainly measure individual behaviour data at different spatiotemporal scales using spatial, temporal, and individual attributive data. To assess the impact of those technologies, it is vital to define different scales of intervention of new AI and urban extensive data analysis, starting from local fine-grained analyses of urban spaces such as streets and plazas (possibly due to geolocation) through the neighbourhood and up to the city or even regional scale (allowing to study functional connections).

Regional linkages and polycentric spatial structure analyses can help to reflect complex features such as mobility and ambiguity and to illustrate spatiotemporal dynamics. 

• Urban spatial structure and dynamic analyses using data with high frequency allow for the study of the growing dynamic and liquidity of the spatial structure of cities and, at the same time, allow for a refinement of spatiotemporal interactions such as individual user trajectories.
• Urban flow analyses allow the study of patterns embedded in the network of MPD interaction and mobile phone holders' movements and, due to their massive volume and high frequency of data, can support transport system optimization and spatial structure improvements.
• Analyses of urban morphology can reduce the need for extensive fieldwork, e.g., interviews, neighbourhood tours, and expert consultation, as analyses of large volumes of data (e.g., images, with AI algorithms) allow for the evaluation of public spaces and the creation of typologies based on extensive samples.
• Analyses of the behaviour and opinion of urban dwellers could help in reflecting fixed features, e.g., age, gender, occupation, and other dynamic attributes at the spatiotemporal scale: preference, emotion, and satisfaction of individuals.

Urban health, microclimate, and environment analyses can support the transition into more resilient urban structures through the extension of traditional data sources to include user-generated content and data from participatory action research.

Conclusion

Urban planning, in short, deals with solving the problems of modern society. The issues are complementary to the growing population in today's society. The problems in society range from mundane tasks like ensuring sanitization to more technical functions like managing infrastructures. The concept of smart cities has been a topic of great interest for social scientists, engineers, and everyone who wants to integrate technologies into their daily lives. AI and IoT have become an essential part of our lives. Data has become ubiquitous with such smart devices connected to the internet.