Goiania Project

Goiania Brazil

Simulating Future Infrastructure Costs for IDB

The Interamerican Development Bank (IDB) is interested in helping mid-sized cities throughout Latin American and the Carribean to grow in a more sustainable fashion. As a pilot project, we focused on the issue of heavy infrastructure extension costs, because these costs are a key policy driver. We studied the city of Goiania Brazil, which is growing extremely rapidly, and well beyond prior plans.

Geodesign Technologies developed two products supporting this initiative: a simplified urban growth model which could be parameterized largely using satelite imagery analysis, and a set of heavy infrastructure extension cost models for roads, BRT and sewers.

Process

Like most rapidly-growing city regions worldwide, Goiania lacked a consistent and comprehensive inventory of historic urban growth. Therefore, we worked with our business partner GEC to generate maps of land cover in 1985, 2000 and 2010. These maps were used to measure the relationships between population and land use over time. They were also used to calibrate a simplified version of the AttCon Urban Growth Model. In this case, time and budget did not allow for a participatory modeling process or multiple scenarios, so we developed a single "plan/trend" scenario based on recent growth patterns.

For each type of heavy infrastructure, we conducted a series of local expert interviews, reviewing relevant cost factors. From these interviews and supplementary research, we created spatial impact models which predict the cost of infrastructure extension required to maintain a fixed level of service. These models take into account existing infrastructure, and development type and density.

Results

From our satelite image analyses, we found that urban footprint of the city has increased at approximately 10,000 hectares or 20% per decade over the last 30 years. This rate is increasing overall, driven by an increase in the share of "low density" development from 5% to 50% in the last decade. More detailed spatial and temporal analysis showed one major factor to be the recent creation of an urban ring road. These patterns were not surprising, and are indeed similar to those found worldwide. However in projecting future urban growth at regional scales, it is essential to develop a locally-calibrated model.

Our analysis of current infrastructure and its extension costs found a strong correlation between development density and service costs per household. Again, this is not a surprising finding, but its locally-scaled quantification is important because it supports spatially-specific planning recommendations. We found that plan/trend scenario urbanization patterns will cost approximately $1 billion Reis (~US$500 million) over the next 50 years, and that this cost is highly sensitive to the spatial relationship between density and infrastructure.

Basic planning measures such as organizing the existing density mix along transit corridors was found to save at least 15% overall. Planning future areas with a density mix more similar to that built in the 1985-2000 period would result in even more substantial savings, likely in the 50% range. More refined planning would require the development of additional spatial scenarios with local participation. However there is strong indication that this would be worthwhile in that the return on investiment in planning would exceed 100:1.

Conclusions

This project shows that even rapid and basic simulation of urban footprints and associated infrastructure costs can be of significant value to cities and regions. Given the recent advent of universal access to the necessary remote sensing data, this can be done anywhere in the world, regardless of prior digital information availability. Benefits on the order of hundreds of millions of dollars can be expected for mid-sized cities and surrounding regions. Conversely, the construction of major urban infrastructure such as ring roads without consideration of their impacts on future land use can lead to large fiscal strains or serious reductions in levels of service per capita.

Credits

This project was conducted by Dr. Flaxman as a partner of GeoAdaptive LLC. Remote sensing work was conducted by the Global Ecosystems Center supervised by Gary Moll.