地理科学进展 ›› 2022, Vol. 41 ›› Issue (2): 251-263.doi: 10.18306/dlkxjz.2022.02.006
刘吉祥1(), 肖龙珠2,*(
), 周江评1, 郭源园3, 杨林川4
收稿日期:
2021-02-07
修回日期:
2021-05-25
出版日期:
2022-02-28
发布日期:
2022-04-28
通讯作者:
*肖龙珠(1988— ),女,福建莆田人,博士生,主要从事交通出行行为与轨道交通导向发展(TOD)研究。E-mail: xiaolongzhuu@163.com作者简介:
刘吉祥(1989— ),男,湖南娄底人,博士生,主要从事城市健康地理与交通出行研究。E-mail: u3004679@connect.hku.hk
基金资助:
LIU Jixiang1(), XIAO Longzhu2,*(
), ZHOU Jiangping1, GUO Yuanyuan3, YANG Linchuan4
Received:
2021-02-07
Revised:
2021-05-25
Online:
2022-02-28
Published:
2022-04-28
Supported by:
摘要:
步行不仅是一种原始、便捷的交通方式,同时也是体力活动的重要组成部分,对于提升公共健康、改善交通拥堵和减轻污染排放等均有重要的积极意义。然而,包括青少年在内的城市居民步行比例持续下降,体力活动水平日益降低。青少年正处于身心发育的关键时期,体力活动的缺乏将导致肥胖等慢性非传染病,为其将来发展埋下巨大的健康隐患。如何通过对建成环境进行干预,提高青少年步行通学比例,从而提高其体力活动水平,引起了不少学者的关注,取得了较为丰硕的研究成果。然而,既有研究存在以下不足:第一,大部分已有研究以西方城市为案例,很少研究关注中国城市;第二,绝大部分既有研究基于线性或广义线性的假设考察建成环境对步行通学的影响,很少研究关注两者之间的非线性关系。鉴于此,论文以厦门岛为案例,基于极限梯度提升模型,考察青少年家和学校建成环境对其步行通学的影响。研究发现:① 通学距离是影响青少年步行通学最重要的因素,其相对贡献接近4成(39.99%);② 建成环境(以5Ds模型表征)作用显著,家、校建成环境相对贡献合计达36.28%,超过社会经济属性(23.73%),离市中心的距离和道路交叉口密度等变量具有重要作用;③ 全部建成环境变量和主要社会经济属性变量均与青少年步行上学存在非线性关系,且存在明显的阈值效应。研究为城市决策者关于提高青少年步行通学倾向提供了丰富的政策启示。
刘吉祥, 肖龙珠, 周江评, 郭源园, 杨林川. 建成环境与青少年步行通学的非线性关系——基于极限梯度提升模型的研究[J]. 地理科学进展, 2022, 41(2): 251-263.
LIU Jixiang, XIAO Longzhu, ZHOU Jiangping, GUO Yuanyuan, YANG Linchuan. Non-linear relationships between the built environment and walking to school: Applying extreme gradient boosting method[J]. PROGRESS IN GEOGRAPHY, 2022, 41(2): 251-263.
表1
自变量描述性统计
变量 | 定义/描述 | 均值(标准差)或百分比 |
---|---|---|
社会经济属性 | ||
年龄 | 受访者年龄(岁) | 11.09 (8.32) |
性别 | 1=女,0=男 | 0=48.0%, 1=52.0% |
住房面积 | 家庭住房面积(m2) | 79.77 (46.37) |
住房性质 | 分类变量(1=自有, 2=单位住房, 3=租住) | 1=81.3%, 2=4.5%, 3=14.2% |
家庭规模 | 家庭成员数量(人) | 4.85 (3.22) |
户口 | 1=有厦门户口,2=无厦门户口 | 0=9.8%, 1=90.2% |
拥有小汽车 | 1=所在家庭拥有至少1辆小汽车,0=无 | 0=83.7%, 1=16.3% |
父/母以步行通勤 | 1=父母亲至少一人以步行为通勤方式,0=无 | 0=81.17%, 1=18.83% |
有人接送 | 1=有长辈接送,0=无 | 0=70.11%, 1=29.89% |
出行特征 | ||
通学距离 | 受访者通学实际距离(km) | 1.24 (1.68) |
建成环境 | ||
人口密度 | 人口数量/TAZ面积(人/km2) | 13194.87 (10325.10) |
容积率 | 总建筑面积/TAZ面积 | 0.88 (0.56) |
土地利用混合度 | 14种主要的土地利用类型(包括居住、工业、教育、行政办公、休闲娱乐、公共开放空间等)的数量及比例,采用改良版的熵值法计算 | 0.58 (0.16) |
交叉口密度 | 道路交叉口数量/ TAZ面积(个/km2) | 82.88 (64.35) |
公交密度 | 公交线路数量/ TAZ面积(条/km2) | 69.44 (62.78) |
离市中心距离 | 从TAZ中心到CBD(即中山路)的路网距离(km) | 8.78 (4.07) |
表2
模型表现
模型 | 操作平台 | 最佳迭代数 | 训练误差 | 测试误差 | 预测准确率/% | AUC值 |
---|---|---|---|---|---|---|
XGBoost | R, “XGBoost” | 9931 | 0.1258 | 0.1713 | 82.88 | 0.892 |
GBDT | R, “caret” | 6554 | 0.2188 | 0.2149 | 78.51 | 0.816 |
RF | R, “randomForest” | 500 | 0.1561 | 0.1789 | 82.11 | 0.848 |
LightGBM | Python, “lightGBM” | 19821 | 0.1610 | 0.1937 | 80.63 | 0.803 |
Adaboost | R, “adabag” | 500 | 0.1686 | 0.1923 | 80.77 | 0.798 |
Logistic | R, “glm” | — | 0.2243 | 0.2396 | 76.04 | 0.743 |
表3
自变量相对重要性
变量 | 相对重要性/% | 排名 |
---|---|---|
出行特征 | ||
通学距离 | 39.99 | 1 |
社会经济属性 | ||
住房面积 | 7.84 | 2 |
年龄 | 7.13 | 3 |
有人接送 | 2.52 | 15 |
家庭规模 | 1.42 | 17 |
性别 | 1.14 | 18 |
拥有小汽车 | 1.13 | 19 |
父/母以步行通勤 | 0.97 | 20 |
户口 | 0.81 | 21 |
住房性质 | 0.38 | 22 |
小计 | 23.73 | |
出发地(家)建成环境 | ||
土地利用混合度 | 3.67 | 4 |
离市中心距离 | 3.59 | 5 |
道路交叉口密度 | 3.50 | 6 |
容积率 | 3.28 | 8 |
公交站点密度 | 2.90 | 9 |
人口密度 | 2.88 | 10 |
小计 | 19.82 | |
目的地(校)建成环境 | ||
道路交叉口密度 | 3.45 | 7 |
离市中心距离 | 2.82 | 11 |
公交站点密度 | 2.70 | 12 |
容积率 | 2.69 | 13 |
土地利用混合度 | 2.56 | 14 |
人口密度 | 2.24 | 16 |
小计 | 16.46 |
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