Title
Exposure opportunity index: measuring people-perceiving-greenery at floor-level effectively
Abstract
People prefer staying in neighborhoods with abundant green space, but crowded urban buildings insulate residents from experiencing nature. As people spend most of time indoors, it is necessary to measure the amount of perceived greenery from residents’ perspective. Therefore, measuring people-perceiving-greenery at floor-level is very important not only for the developing of urban landscape ecology but also for urban greenery planning, living environment evaluation and eco-city construction. Exposure Opportunity Index (EOI) based on hierarchy urban landscape was developed to measure people-perceiving-greenery at different floors. It considered spatial relations between building floors and nearby greenery. Two main steps were involved: first, the hierarchy urban landscape model was developed including extracting the urban structural information such as Canopy Height Model (CHM) and building structure, and conducting the spatial stratification strategy. Second, EOI was calculated with Inverse Distance Weighting (IDW) method. Over 200 building storeys’ EOI were calculated and validated with 340 photos. Results indicated that EOI fit well with the reference data extracted from the photo series, and it was effective and reliable in measuring people-perceiving-greenery at floor-level. Furthermore, EOI may play important role in urban green planning and residential amenity evaluation.
Year
DOI
Venue
2020
10.1007/s12145-019-00410-2
Earth Science Informatics
Keywords
Field
DocType
Exposure opportunity index, People-perceiving-greenery, Hierarchy urban landscapes, Floor-level
Spatial relation,Data mining,Environment Evaluation,Landscape ecology,Environmental resource management,Computer science,Inverse distance weighting,Amenity,Hierarchy
Journal
Volume
Issue
ISSN
13
1
1865-0473
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Qingyan Meng1235.52
Xu Chen200.34
Yunxiao Sun300.34
Jiahui Zhang400.34
Qiao Wang59721.94
Tamas Jancso6192.81
Shunxi Liu700.34