Abstract
In this study, we analyze the quality of water hydrant data for estimating housing vacancies based on their spatial relationships with the other geographical data that we consider are correlated with such vacancies. We compare with in-situ vacant house data in several small districts, thus verifying the applicability of the water hydrant data to the detection of vacant houses. Through applying Bayesian approach, we apply the water hydrant data and other geographical data to repeatedly Bayesian updating for the classification of vacant / no vacant houses. We discuss the results of this classification using the temporal intervals associated with turning off metering, fluctuations in local population density, the densities of water hydrants as indicators of vacancies and several other geographical data. We also conduct the feasibility study on visualisation for the estimation results of housing vacancy distributions derived from the fine spatial resolution data.
Highlights
A key housing problem in many large cities in the developed world involves high levels of vacancies that are spatially clustered
We pointed out the possibility of utility data for applying the estimation of vacancy distributions
We adapted water hydrant data as the utility data, and we verified relationship between turned-off water hydrants and vacant houses identified by filed survey
Summary
A key housing problem in many large cities in the developed world involves high levels of vacancies that are spatially clustered. There are many causes that generate high vacancy rates, ranging from housing in large cities being regarded by mobile capital as an asset class, with luxury homes kept empty in order to maximize the performance of asset management (Gerald, 2005, Norris, 2009, Hoekstra, 2011, Vakili-Zad, 2011) Another key factor is population aging (Deilmann, 2009, Sasaki, 2010, Yamamoto, 2011). It is possible to recognize vacant houses where data shows their connection to utility lines such as electricity, water supply, possibly telephones and increasingly broadband services This lifeline network data is usually recorded and available over long periods at a fine spatial resolution (at street address) where it is maintained by local government and supply companies.
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