Supporting Information for Tracking progress towards urban nature targets using landcover and vegetation indices: A global study for the 96 C40 Cities

These datasets include the results published in Martin, G.K., K. O'Dell, P.L. Kinney, M. Pescador Jimenez, D. Rojas-Rueda, R. Canales, and S.C. Anenberg (2024). Tracking progress towards urban nature targets using landcover and vegetation indices: A global study for the 96 C40 Cities. GeoHealth, In press.

Supplmental Data S1 contains infomration for the main analysis using Urban Centre Database bounds and includes city-level summary measures. These include measures of natural space and population as well as model diagnostics and outputs. Supplmental Data S2 is a parallel dataset of the sensitivity analysis using the self-defined C40 urban bounds. 

Variable definitions:

city= name of urban area

country= country in which urban area is located

region= C40-defined global region in which urban area is located

t1_mean_ndvi= city-mean NDVI. The mean of the 100m pixel NDVI values within the urban bounds. This value was used to assess Target 1.

t1_mean_mndvi= city-mean NDVI plus water. The mean of the 100m pixel NDVI plus water values within the urban bounds. Water pixels were assigned a value of 1.

t1_mean_ga= city-mean proportion of green area. The mean of the 100m pixel green area values within urban bounds. This value was used to assess Target 1.

t1_mean_gba= city-mean proportion of green or blue area. The mean of the 100m pixel green or blue area values within the urban bounds.

t2_mean_ndvi= city-mean blurred NDVI. The mean NDVI within a 1000m buffer area of each 10m pixel was first calculated and then aggregated to 100m. This is the mean of each 100m pixel’s blurred NDVI value within urban bounds.

t2_mean_mndvi= city-mean blurred NDVI plus water. The mean NDVI plus water within a 1000m buffer area of each 10m pixel was first calculated and then aggregated to 100m. This is the mean of each 100m pixel’s blurred NDVI plus water value within urban bounds. This value was used to assess Target 2.

t2_mean_ga= city-mean blurred green area. The mean green area within a 1000m buffer area of each 10m pixel was first calculated and then aggregated to 100m. This is the mean of each 100m pixel’s blurred green area value within urban bounds.

t2_mean_gba= city-mean blurred green or blue area. The mean green or blue area within a 1000m buffer area of each 10m pixel was first calculated and then aggregated to 100m. This is the mean of each 100m pixel’s green or blue area value within urban bounds. This value was used to assess Target 2.

city_adult_pop= total adult population within urban bounds. The sum of the population aged 20 or older living within each 100m pixel within the urban bounds.

pct_access_gba= percent of adult population with access to green or blue space within a 1000m buffer.

t1_adjr2= adjusted R2 value for each city’s Target 1 regression model

t1_rmse= rmse value for each city’s Target 1 regression model

t1_p_ndvi_30= the predicted NDVI value equivalent to achieving 30% green area value from each city’s Target 1 regression model

t1_p_ndvi_40= the predicted NDVI value equivalent to achieving 40% green area value from each city’s Target 1 regression model

threshold_reg= the predicted NDVI value equivalent to 100% green area value from each city’s Target 1 regression model. This value was used in a sensitivity analysis to determine whether a pixel was considered “green” for Target 2.

threshold_reg75= the predicted NDVI value equivalent to 75% green area value from each city’s Target 1 regression model. This value was used to determine whether a pixel was considered “green” for Target 2.

threshold_reg90= the predicted NDVI value equivalent to 90% green area value from each city’s Target 1 regression model. This value was used in a sensitivity analysis to determine whether a pixel was considered “green” for Target 2.

t2_adjr2 = adjusted R2 value for each city’s Target 2 regression model

t2_rmse= rmse value for each city’s Target 2 regression model

t2_p_mndvi_70= the predicted NDVI plus water value equivalent to 70% of the 100m pixels having access to green or blue area within 1000m from each city’s Target 2 regression model

t1_yes_30= city meets Target 1 (30% green area). 0=no, 1=yes

t1_yes_40= city meets Target 1 (40% green area). 0=no, 1=yes

t2_yes= city meets Target 2 (70% of population has access to green or blue area within 1000m). 0=no, 1=yes

本数据集收录了Martin、G.K.、K. O'Dell、P.L. Kinney、M. Pescador Jimenez、D. Rojas-Rueda、R. Canales及S.C. Anenberg（2024）发表的研究成果，论文题为《基于土地覆盖与植被指数追踪城市自然目标进展：针对96个C40城市（C40 Cities）的全球研究》，发表于《GeoHealth》，即将刊出。

补充数据S1包含采用城市中心数据库（Urban Centre Database）边界开展的核心分析相关信息，涵盖城市层面汇总统计指标，包括自然空间与人口相关指标，以及模型诊断结果与模型输出。补充数据S2为采用自定义C40城市边界开展的敏感性分析的平行数据集。

变量定义如下：
city= 城市区域名称
country= 城市区域所属国家
region= C40划定的城市所在全球区域
t1_mean_ndvi= 城市平均归一化植被指数（Normalized Difference Vegetation Index, NDVI）：城市边界内100米分辨率像素的NDVI均值，该指标用于评估目标1。
t1_mean_mndvi= 城市平均归一化植被指数（含水体）：城市边界内100米分辨率像素的NDVI（含水体）均值，其中水体像素赋值为1。
t1_mean_ga= 城市平均绿地面积占比：城市边界内100米分辨率像素的绿地面积均值，该指标用于评估目标1。
t1_mean_gba= 城市平均绿蓝空间占比：城市边界内100米分辨率像素的绿地或蓝地面积均值。
t2_mean_ndvi= 城市平均平滑化归一化植被指数：首先计算每个10米分辨率像素周边1000米缓冲区的NDVI均值，再聚合至100米分辨率，最终得到城市边界内各100米像素的平滑化NDVI均值。
t2_mean_mndvi= 城市平均平滑化归一化植被指数（含水体）：首先计算每个10米分辨率像素周边1000米缓冲区的NDVI（含水体）均值，再聚合至100米分辨率，最终得到城市边界内各100米像素的平滑化NDVI（含水体）均值，该指标用于评估目标2。
t2_mean_ga= 城市平均平滑化绿地面积占比：首先计算每个10米分辨率像素周边1000米缓冲区的绿地面积均值，再聚合至100米分辨率，最终得到城市边界内各100米像素的平滑化绿地面积均值。
t2_mean_gba= 城市平均平滑化绿蓝空间占比：首先计算每个10米分辨率像素周边1000米缓冲区的绿蓝空间面积均值，再聚合至100米分辨率，最终得到城市边界内各100米像素的绿蓝空间面积均值，该指标用于评估目标2。
city_adult_pop= 城市边界内成年总人口：城市边界内各100米分辨率像素中年龄20岁及以上的人口总和。
pct_access_gba= 周边1000米缓冲区内可接触绿蓝空间的成年人口占比。
t1_adjr2= 各城市目标1回归模型的调整决定系数（adjusted R²）值
t1_rmse= 各城市目标1回归模型的均方根误差（Root Mean Square Error, RMSE）值
t1_p_ndvi_30= 各城市目标1回归模型中，对应30%绿地面积占比的预测NDVI值
t1_p_ndvi_40= 各城市目标1回归模型中，对应40%绿地面积占比的预测NDVI值
threshold_reg= 各城市目标1回归模型中，对应100%绿地面积占比的预测NDVI值，该指标用于敏感性分析以判定像素是否被纳入目标2的“绿地”范畴。
threshold_reg75= 各城市目标1回归模型中，对应75%绿地面积占比的预测NDVI值，该指标用于判定像素是否被纳入目标2的“绿地”范畴。
threshold_reg90= 各城市目标1回归模型中，对应90%绿地面积占比的预测NDVI值，该指标用于敏感性分析以判定像素是否被纳入目标2的“绿地”范畴。
t2_adjr2= 各城市目标2回归模型的调整决定系数值
t2_rmse= 各城市目标2回归模型的均方根误差值
t2_p_mndvi_70= 各城市目标2回归模型中，对应1000米缓冲区内70%像素可接触绿蓝空间的预测NDVI（含水体）值
t1_yes_30= 城市是否达成目标1（30%绿地面积占比），0=否，1=是
t1_yes_40= 城市是否达成目标1（40%绿地面积占比），0=否，1=是
t2_yes= 城市是否达成目标2（70%成年人口可在1000米范围内接触绿蓝空间），0=否，1=是