Addressing the data-practice feedback loop in land use modeling for the Mumbai Metropolitan Region
Authors: Rajeev Lokare Sayali , Krishnan Supriya , Van Delden Hedwig , Ekbote Abhijit 
Published: March 2024

Land use change modeling is a complex process that necessitates considering factors such as governmental policies, bureaucratic and political influences, private real estate markets, and human settlement patterns. In regions with limited data availability, like South Asia, constructing accurate quantitative models becomes particularly challenging, despite their potential benefits. The rapid pace of urbanization, coupled with long planning cycles, widens the gap between data acquisition and practical implementation. Modelers often have to rely on inputs from stakeholders and experts, emphasizing the need for a more resource-efficient and expedited feedback loop between data and practice.
This study underscores the establishment of a robust data to practice knowledge feedback loop to enhance the effectiveness of quantitative land-use models, to capture the dynamics of rapidly evolving urban areas. Such a loop can facilitate improved land use models that serve as powerful decision-making tools for urban environments and addressing challenges posed by climate change.
In this study, we investigate strategies to strengthen the data-practice feedback loop using our ongoing work in developing a land use model for the Mumbai Metropolitan Region (MMR), one of the world's fastest-growing regions and one that highlights the demand for data-driven resilience planning. Our methodology employs a spatially explicit land use modeling framework (Metronamica) supported by a cellular automata (CA) algorithm. This framework accommodates various drivers and processes crucial for evaluating land use dynamics across sectors and incorporates expert inputs during model setup.&#160;
The limited research in cellular automata for the South Asian context is due to intricacies of land use interactions, data scarcity, focus on economics by more complex CA models, and the lack of transparency in artificial neural network models, all of which pose challenges for their use in planning. To address these concerns, our study adopts a hybrid approach, combining a foundational CA model with automation and expert inputs to create a faster, traceable, and nuanced model.
We also highlight the challenges of building a land-use model as robust evidence for planners, examining aspects of data availability, accessibility, data processing and digitization, and format variations. Additionally, we discuss the use of Python modules to automate model calibration and emphasize the importance of expert feedback in refining the model and translating policies into quantitative inputs.
The methods and insights emerging from this study shed light on critical questions, such as defining the criteria for a "good enough" land use model, integrating on-ground expertise into quantitative modeling, and improving data processes to support more efficient, rapid, and accurate model setups. This approach aims to bridge the gap between data acquisition and practical application in a resource-efficient manner.
In conclusion, we advocate for the adoption of mixed data creation to develop useful land-use models for emerging regions. We anticipate that the insights from this study will contribute to the establishment of more robust and concise data-practice feedback cycles in data-scarce urban environments. This, in turn, can empower data-driven decision-makers to incorporate their expertise into quantitative modeling, fostering adaptable and resilient cities while giving decision-makers greater control over the process.