Remotely-Sensed Urban Environmental Indices and their Economic Implications
ISBN 978-85-88783-11-9
Authors
1Jiao, L.; 2Jin, J.; 3Liu, Y.; 4Liu, Y.
1WUHAN UNIVERSITY Email: lmjiao027@163.com
2WUHAN UNIVERSITY
3WUHAN UNIVERSITY
4WUHAN UNIVERSITY
Abstract
Explicitly characterizing and assessment of urban environment amenity will lend support to urban planning. It is difficult to develop proper indices to characterize indescribable urban environment amenity. Taking a metropolis in central China as a case, we develop and discuss two kinds of indices to characterize urban environmental amenity based on remote sensing image, and examine their economic effects on housing price using hedonic modeling. The environmental indices that are generally accepted and spatially continuous have rarely been introduced in hedonic modeling before. These two kinds of indices are linked to urban heat islands and vegetation coverage, respectively. The initial motivation is based on two basic assumptions. One is that urban heat islands are the compositive reflection of many unfavorable urban environmental factors, such as industries, traffic congestion, lack of vegetation, and not properly planned constructions. The other one is that good vegetation coverage is a commonly accepted favorable urban environmental factor. We aim to build consistent environmental indices based on remotely sensed information and test their application in hedonic modeling. Landsat TM images are used to retrieve land surface temperature (LST) and normalized vegetation index (NDVI). We use neighborhood analysis to derive the two kinds of indices based on LST and NDVI map, namely, heat island index and vegetation coverage index. The indices are equal to the average of normalized LST and NDVI in a certain buffer area, respectively. The indices consistently measure the degree of urban environmental amenity at any location. We analyze the influence of buffer distance on the indices. We find that they are both robust when the buffer distance is from 100 to 300 meters, and the LST-based indices are relatively more robust than the NDVI-based ones. We produce maps of the indices to illustrate the distribution of urban environmental amenity. Spatial autocorrelation analysis of the indices shows that they are spatially autocorrelated. Bivariate Moran’s I analysis of the two kinds of indices shows that they are slightly spatially correlated with each other. We further build the hedonic model using spatial lag regression between apartment price and explanatory variables including the proposed environmental indices and other locational factors, as well as apartment structural variables. The locational factors include proximity to business centers, accessibility to roads and public transportation stops, proximity to rivers, lakes and parks. The structural variables include floor height, floor area ratio, number of bedrooms, and number of bathrooms. The results show that proximity to large open spaces, distance to business centers, floor area ratio, and floor height can exert significant and positive influence on housing price. The LST-based heat island index is found to have negatively significant influence on housing price. One percent increase in heat island index will decrease housing price by about 67.6 Yuan/m2. It means that the heat island environments including local islands in urban area are unfavorable for apartment buyers. We further investigate the differences of housing price between the locations inside heat islands of different levels and the locations outside heat islands, and confirm our findings from hedonic modeling. The heat island index proposed in this study can be used as a quantitative and compositive index that represents the urban environmental amenity. The NDVI-based vegetation coverage index is unexpectedly found that it is negatively related to housing price. When we refine the interest area we observe the positive effects of vegetation coverage index. This indicates that vegetation coverage index does not increase housing price consistently. Finally we produce the maps of the economic effects of the environmental indices.
Keywords
Spatial Regression; Urban Heat Islands; Remote sensing