## 2 Data Analytics and Machine Learning Methods in Urban Planning and Design  
Data analytics refers to examining a large set of data to uncover patterns, correlations, trends, and valuable insights. It involves applying various statistical and computational techniques to understand and interpret data in order to make informed decisions, solve problems, or identify opportunities for improvement [22]. [PERSON] and [PERSON] [23] identified the characteristics of data analytics in urban planning as follows: (i) interpreting patterns; (ii) deploying computing technologies; (iii) capturing real-time and dynamic changes; and (iv) capturing the compound nature of the data and their mutual impact on each other. Data analytics methods can be employed to understand urban dynamics, assess current status, and predict future trends. Data analytics methods involve descriptive and exploratory analysis, predictive analytics (which includes ML methods), and prescriptive analytics (which includes AI algorithms). Descriptive analytics provides insights into the current state of urban areas [24]. In the context of urban planning, the collected data are KPIs related to area utilization, infrastructure distribution, and environmental factors. Exploratory analytics try to identify patterns within urban data to uncover insights and trends. These techniques are used to analyze the distributions of population, area utilization, and infrastructure. The trends indicate the impact of changes resulting from urban development and provide a guide for planning transportation and infrastructure development. For example, [PERSON] et al. [25] utilized such analysis in their study to indicate urban design factors influencing cycling.  
Predictive analytics involves predicting and forecasting future trends based on historical data using ML and time series, such as the forecasting of generated waste and gas emissions. These predictions enable policymakers to address future resulting challenges and plan proactive actions [26]. Finally, prescriptive analytics recommends actions to optimize urban development and resource allocation, such as optimization models to enhance public transportation routes or waste collection activity. Likewise, simulation models evaluate different scenarios and their impacts, guiding decisions on infrastructure investment and land use planning [27]. Particularly in the area of predictive analytics, ML methods offer powerful tools for analyzing complex urban data and predicting future trends [28]. These techniques are employed to build predictive models for various urban scenarios, such as regression models for predicting gas emissions based on historical data. Additionally, ML models can predict air quality degradation, which helps policymakers develop strategies that mitigate the impacts of climate change. These predictive models provide valuable insights into future urban dynamics, allowing for proactive actions. ML algorithms are capable of recognizing patterns and correlations within data. Thus, indicators could be used to classify urban areas based on their similarities.  
Various data analytics and ML techniques could be employed in the context of a large-scale and international project to understand urban dynamics, assess current status, and predict future trends. The integration of ML in the analysis of data speeds up the evaluation process, allowing for more efficient policy recommendations and resource optimization. According to [29], when KPIs are combined with ML, decision-making accuracy and efficiency are greatly enhanced, ensuring actions are aligned with strategic objectives and minimizing biases.