Test the Effectiveness of the Open Spaces Scenario in Promoting Socio-economic Development

The preservation of open spaces is treated as an important policy in recent years as urbanization level is increasing higher in the world (Geoghegan, 2002). There are multiple positive effects associated with open spaces, including recreation, aesthetic and environment values (Geoghegan, 2002). The positive effects of open space as a nature-based solution on urban social, economic and environmental factors have been explored by a number of previous papers, such as housing price (Lutzenhiser & Netusil, 2001; Bolitzer & Netusil, 2000), spatial pattern (Lewis et al., 2009; Irwin & Bockstael, 2004), human health (Groenewegen et al., 2006; Irvine et al., 2013) and social safety (Groenewegen et al., 2006; Fischer et al., 2004). However, relatively less papers have predicted the open spaces’ influences on socio-economic development. This paper will firstly verify the open space influences on economic factor (housing sale prices) and social factor (sense of safety, residential agglomeration) using a linear regression model. We consider the housing attributes, urban form attributes (eg. population density, block size, road density), driving and walking accessibility to different types of public open spaces, and accessibility to other amenities (eg. hospitals and schools) as influential features. Then, we test several machine learning algorithms in predicting the housing price and sense of safety change based on future open space planning scenarios, and choose the most suitable machine learning algorithm. City of Chicago, Illinois, US is chosen to be study area since data availability, sufficient open space types and long-term open space preservation strategies. This study can quantify the values of the open spaces in influencing socio-economic developments and provide a way to test the open space scenarios. It has potential to work as a tool for local planners to make better nature-based solutions in open space designs and plans.