Urban analytics is the systematic process of applying advanced data science techniques, computational methods, and interdisciplinary insights to analyze the dynamics of cities and urban environments. In the context of sustainable development and smart cities, it provides a framework for understanding how urban systems function and how data-driven strategies can make them more efficient, inclusive, and resilient. The concept of urban analytics has emerged with the growth of digital technologies, the proliferation of data sources such as sensors, social media, and satellite imagery, and the increasing importance of evidence-based policy-making in urban planning. By integrating data with urban theory, urban analytics allows decision-makers to model, predict, and design interventions that support the goals of sustainable urban growth and smart governance. The salient features of urban analytics are rooted in its interdisciplinary nature and its ability to harness both structured and unstructured data for urban problem-solving. One of the most important features is its reliance on big data. Modern cities generate vast quantities of information from transport systems, energy grids, waste management, healthcare services, and citizen interactions. Urban analytics leverages these large-scale datasets to identify patterns of consumption, mobility, and social behavior that are often invisible in traditional urban studies. For example, transport data can be analyzed to optimize bus routes and reduce congestion, directly contributing to sustainable mobility and energy efficiency in smart cities. Another key feature is the use of geospatial analysis. Spatial data forms the backbone of urban analytics because cities are inherently spatial entities where activities are distributed across physical landscapes. Tools such as Geographic Information Systems (GIS), remote sensing, and spatial statistics allow researchers to map urban growth, identify vulnerable zones, and track resource allocation. In the sustainable development framework, geospatial analytics helps policymakers address challenges like urban sprawl, slum formation, or climate vulnerability by providing location-specific insights. For instance, mapping air quality across a city enables targeted interventions in pollution hotspots, improving overall urban health and livability. Predictive modeling is another salient characteristic of urban analytics. Through machine learning algorithms, statistical forecasting, and simulation models, urban analysts can anticipate future urban scenarios. These predictive insights are valuable for smart city planning, where infrastructure investments must align with long-term growth and sustainability goals. Predictive analytics can estimate traffic flows, housing demand, water consumption, or even disease outbreaks, enabling proactive governance. For sustainable development, this feature is particularly important because it shifts urban management from reactive problem-solving to preventive planning, ensuring cities remain adaptable to changing social, economic, and environmental pressures. Citizen-centric