The 21st century is the century of cities. While in 1950 only 761 million people lived in cities, the urban population grew to 4.2 billion in 2018, accounting for more than half of the world’s population [1
]. According to the United Nations, this percentage is likely to grow significantly in decades to come. The recently published World Urbanization Prospects estimates that by 2050, almost 70% of the world’s population will be urban, with the fastest growth taking place in Africa and Asia [1
]. Cities are the hubs of economic development, technological innovation, and entrepreneurship. They also provide a multitude of services to inhabitants (e.g., adequate housing, health care, social services, education, and infrastructure) that are often lacking or scarce in rural areas [2
]. Over the past decades, urbanization has been a driver of economic development and poverty reduction in many places around the world [3
]. Hence, in an increasingly urban world, the successful management of current and future urban growth has direct implications for sustainable development, as encapsulated by Sustainable Development Goal (SDG) 11 (Sustainable Cities and Resilient Communities), which commits to making cities inclusive, safe, resilient, and sustainable. However, meeting these commitments under SDG 11 remains a challenging task, given that the most rapid urbanization in coming decades will occur in low-income and lower-middle-income countries, where adequate capacity and resources to manage such a rapid transformation are in short supply. Furthermore, cities in these countries are often growing in places highly exposed to environmental- and climate change-related hazards, which poses additional challenges for sustainable development [4
]. Therefore, sustainable policies and strategies for planning and managing urban growth are needed to ensure that the benefits of urbanization are leveraged and shared.
Historically, human decisions on urban planning are often based on mental scenarios for desired/undesired future events [5
]. However, modern technology has enabled us to materialize such layouts through modeling. The resultant simulations can support a detailed analysis of possible alternative outcomes and can help to plan the urban future accordingly. Thus, future projections and scenarios of urban growth can bolster the planning process by offering local governments the possibility to anticipate future challenges and facilitate the sustainable planning of possible urban futures [6
]. Urban growth modeling provides tremendous opportunities for such tasks and draws on several decades of model development, with the earliest models dating back to the late 1960s [7
]. Urban growth models can support urban planning by providing useful insights into the spatial evolution of cities. However, urban growth is a process that depends on a complex set of environmental, physical, and societal variables. Multiple interactions between these variables can steer urban expansion (or contraction) in different spatially-explicit directions. Therefore, purely data-driven models that rely on a historical series of quantifiable physical variables may not be sufficient to produce a holistic and complex representation of urban processes. The inclusion of local stakeholders in model development allows the introduction of profound assumptions grounded in the local context. These assumptions can help to explore alternative spatial evolutions of the city and to ensure that local issues (e.g., different development scenarios or policy choices) are adequately represented in the subsequent tools [8
]. Efforts to simulate urban growth at the global scale (e.g., [9
]) and high-income countries [11
] are well documented in the literature. Applications in low and lower-middle-income countries are also becoming more frequent [13
]. However, the majority of urban growth simulations at the local level are based on data-driven approaches and associated scientific assumptions [8
], and often do not take into account relevant social, economic, or political drivers of growth, which cannot be represented through quantitative data. Nor do they sufficiently consider the participation of relevant local stakeholders in model development. The inclusion of relevant stakeholders in the design and construction of such models is of utmost importance to (i) ensure the consideration of local conditions in model development, (ii) increase trust in model outputs, and (iii) facilitate the mainstreaming of these outputs into local urban planning and management. Such inclusion is even more relevant in developing countries, where urbanization processes are extremely dynamic and dependent on a combination of social, economic, and governance-related factors that can be only partially captured by purely data-driven approaches [14
The present study addresses some of the above-mentioned gaps by presenting a participatory modeling application to simulate future urban growth of the city of Monastir, Tunisia. Through the visualization of alternative urban futures for the city (co-developed in a participatory process with local stakeholders), the study tests and evaluates the outcomes of rival urban policies under different socio-economic conditions. The article presents future urban growth simulations (2030) for: (1) a business-as-usual (BaU) prediction and (2) four alternative scenarios derived from participatory scenario workshops. Additionally, it discusses the advantages of integrating a data-driven approach with a participatory scenario design.
4. Discussion and Conclusions
This study presents an innovative urban growth simulation that combines traditional data-driven SLEUTH urban growth modeling with participatory stakeholder involvement to simulate possible future scenarios of Monastir’s urban expansion until 2030. The outcomes of the model display a decent performance through an efficient extraction of past growth patterns for future projection [23
]. In addition, the results are presented in easy-to-understand visualizations for the benefit of end users.
Despite these achievements, the presented analysis is not without some limitations. The main drawbacks of the model are the tendency to consider physical over socio-economic parameters of change. Another primary limitation of the model is its computation complexity, which limits its scalability. Moreover, the model did not take into account projections of population and economic growth but used the defined physical historic expansion patterns to project future scenarios. Besides this, the model’s spatial and temporal stationarity also constrain its certainty for long-term prediction (beyond the planning cycle of 2030). Lastly, significant literature studies highlighted that decision-makers tend to misinterpret scenarios through ignoring their uncertainties, which can raise difficulties in communicating the outcomes of future urban growth [28
Nevertheless, this study has attempted to address these limitations. For instance, the novelty of using a participatory approach with local stakeholders reduced the chance of excluding critical (physical) variables for the urban growth simulation, thus improving the representativeness of the model compared to classic data-driven approaches. Moreover, in order to increase the utility of the final product for future land-use policies, Monastir’s official urban planners were involved in the development of the urban growth model. Their participation greatly enhanced the validity of the model for policy-making and ensured improved communication and interpretation of the outcomes. The planners’ perspective was further reflected in the decision to restrict the time range of the application to the duration of the current city plan, thus containing the model’s uncertainty (which would be higher in long-term simulations) and therefore improving its applicability for future-oriented planning. Moreover, the inclusion of relevant stakeholders in the definition of the model proved to be an opportunity to spread awareness about the potential of spatial modeling as a tool for local practitioners of different fields, which the authors hope will lead to future updates of the assessment and the development of local capacities.
In this regard, two significant potential improvements to current applications of the future urban growth model are acknowledged. First, although the application integrated the expectations of local sector-planners to create different scenarios, considerable progress would be made by testing potential alternative sector-based policies and plans and compare their impacts on city growth. Second, while the study addressed the combined urban classes, a potential prospect for future applications would be to estimate the individual growth of the various urban sectors (e.g., residential, commercial, touristic, and industrial).
In conclusion, by integrating a data-driven projection of possible future urban growth with city development pathways derived from participatory workshops, this study shows how and where the city of Monastir could potentially grow until 2030 under alternative scenarios. The analysis reveals that future-oriented growth modeling can play an essential role in testing the effectiveness of potential policies, and can provide vital information to guide the work of local urban planners towards more sustainable urban growth.