000 02342nab a2200193 4500
003 OSt
005 20231003163839.0
007 cr aa aaaaa
008 231003b |||||||| |||| 00| 0 eng d
100 _aIm, Ha Na
_958269
245 _aMeasuring pedestrian volume by land use mix:
_bpresenting a new entropy-based index by weighting walking generation units/
260 _bSage,
_c2020.
300 _aVol. 47, Issue 7, 2020, ( 1219–1236 p.)
520 _aThis study proposes an alternative to the conventional entropy-based land use mix index, which is generally used to measure the diversity of land use. Pedestrian volume was selected as the dependent variable as it represents the vitality of districts, which many recent urban studies now consider important. The study investigates an entropy-based weighted land use mix index, which is weighted by different land use types. For the index, different areas are needed to generate a unit of pedestrian volume, whose measure is m2/person/day. The study demonstrates that this alternative is more effective than the existing conventionally used entropy-based land use mix index for explaining pedestrian volume. The research confirms that the conventionally used entropy-based land use mix index can have a positive or negative impact depending on the land use characteristics of the survey points because the conventionally used entropy-based land use mix index has a non-linear relationship with pedestrian volume. By analysing 9727 surveyed locations of pedestrian volume in Seoul, Korea, the study demonstrates that the weighted land use mix index, rather than the conventionally used entropy-based land use mix index, can improve the explanatory power of the estimation model for the relationship between pedestrian volume and built environments, showing consistent results throughout the empirical analysis. In future built-environment studies, the utility of the weighted land use mix index is expected to improve if studies include how to find the accurate weighting of the land use in estimating the pedestrian volume.
700 _aChoi, Chang Gyu
_958270
773 0 _08876
_917104
_dLondon Pion Ltd. 2010
_tEnvironment and planning B: planning and design (Urban Analytics and City Science)
_x1472-3417
856 _uhttps://doi.org/10.1177/2399808318824112
942 _2ddc
_cEJR
999 _c14832
_d14832