Big data is the hidden key when planning new townships

Soon, the integration of data analytics and artificial intelligence will define the future of township masterplanning. Effective township masterplanning is now more important than ever as cities feel pressures from overpopulation, resource generation, pollution and climate change, and traffic congestion.

Traditionally, township planning relied upon expert knowledge, previous experience and financial expediency, which often led to subjective and questionable decision making. However, decision-makers are increasingly turning to data analytics and artificial intelligence to make informed choices, optimize land use, build efficient infrastructure, and allocate resources effectively.

Predictive modeling

Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of relevant historical data.

In property development and planning, it enables the forecasting of future trends and initiation of data-driven decisions. Predictive models can project population increase, demand for various facilities, and housing requirements by examining past data and recognizing patterns.

Sounds good? The government of Barcelona, Spain thought so too. To plan its upcoming urban development initiatives, the city used predictive modeling techniques. This resulted in optimized land allocation, the building of efficient transportation networks, and preparation for future expansion via studying demographic statistics, economic indicators, and urban mobility trends.

This data-driven strategy resulted in a strong community revival and the establishment of sustainable urban areas.

Data visualization

Predictive modeling is wonderful for people who want to crunch data all day. But what about those of us who just want to see shiny graphs and clean infographics that tell us the story?

Data visualization is critical for improving understanding and communication of complex information. Decision-makers may easily absorb essential insights and detect trends by translating raw data into visual representations such as charts, maps, graphs and graphics. Data visualization enables planners to better communicate their plans and engage stakeholders in the decision-making process—a crucial part of any project.

To include citizens in the masterplanning process, Portland City in Oregon used data visualization tools. Residents were able to explore potential development scenarios via interactive maps and infographics and then submit their opinions. This collaborative approach guaranteed that the final masterplan represented the community’s different needs and ambitions.

Machine learning

We’ve talked about machine learning in this column previously. Algorithms can evaluate massive volumes of data and identify hidden patterns. But how do they fit into the masterplanning process?

Machine learning models can offer optimal resource allocation techniques by recognizing the correlations between numerous variables such as population density, transportation networks, and utility usage.

For example, in its urban planning endeavors, the Singapore government used machine learning algorithms to maximize resource allocation. The models advised techniques for energy-efficient infrastructure design after examining data on population distribution, transportation patterns, and energy use.

In the Philippines, there are now more housing projects under construction than at any other point in history. It is vitally important to not see each as its own island, but as part of a livable, breathable community and ecosystem.

Concrete examples from cities all over the world highlight the practical benefits of data-driven methods in building sustainable, dynamic, and people-centered communities. Soon, the integration of data analytics and artificial intelligence will define the future of township masterplanning, allowing for the building of resilient and successful communities.

The author is the COO and digital co-founder of PropertyAccess

Data visualization is critical for improving understanding and communication of complex information.