本数据集提供了2022年中国城市生态风险评估指数，构建了融合水资源约束的“危险性-暴露度-脆弱性-水资源约束（H-E-V-W）”四维风险评估框架。数据涵盖气候、土地利用、植被、水资源、社会经济等16项二级指标，均来源于权威公开数据库，如遥感产品、气候再分析数据、土地覆盖数据和统计年鉴等。通过归一化处理，并结合层次分析法（AHP）与熵权法（EWM）计算综合权重，最终形成两类城市生态风险指数：未考虑水资源约束的城市生态风险指数（UERHEV）与考虑水资源约束的城市生态风险指数（UERHEVW），并将风险指数归一化后按等间距法划分为五个风险等级（0-0.2为低风险，0.2-0.4为较低风险，0.4-0.6为中等风险，0.6-0.8为较高风险，0.8-1.0为高风险）。", "ds_source": "
气候、植被、土壤等自然环境类数据主要来源于国家青藏高原科学数据中心，土地利用数据源自30m中国土地利用数据（CLCD）产品，社会经济数据采集自国家统计局及各省市统计年鉴，水资源数据来自《中国水资源公报》及各省市水资源公报。", "ds_process_way": "
对所有原始数据进行了统一的投影转换、空间裁剪与归一化处理，并按城市边界聚合计算形成城市尺度的生态风险评估指标和评价结果。为确保指标赋权的科学性与客观性，采用层次分析法（AHP）与熵权法（EWM）相结合的方式确定各指标的综合权重，从而有效避免单一权重方法带来的偏倚。", "ds_quality": "
数据质量良好。", "ds_acq_start_time": "2022-01-01 00:00:00", "ds_acq_end_time": "2022-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.05, "ds_acq_lat_south": 3.85, "ds_acq_lon_west": 73.5, "ds_acq_lat_north": 55.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 38211, "ds_files_count": 2, "ds_format": "excel", "ds_space_res": null, "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "fb412d32-73a6-473d-9738-2d259dedbcd5.jpg", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4599" ], "quality_level": 3, "publish_time": "2025-06-10 14:38:42", "last_updated": "2025-06-10 14:38:42", "protected": false, "protected_to": "2027-06-06 00:00:00", "lang": "zh", "cstr": "11738.11.NCDC.IUELTYJZ.DB6866.2025", "i18n": { "en": { "title": "Dataset for ecological risk assessment of Chinese cities under water resource constraints (2022)", "ds_format": "excel", "ds_source": "
Climate, vegetation, soil and other natural environment data are mainly sourced from the National Qinghai Tibet Plateau Science Data Center. Land use data is obtained from the 30m China Land Use Data (CLCD) product. Socio economic data is collected from the National Bureau of Statistics and statistical yearbooks of various provinces and cities. Water resources data is obtained from the China Water Resources Bulletin and water resources bulletins of various provinces and cities.", "ds_quality": "
The data quality is good.", "ds_ref_way": "", "ds_abstract": "
This dataset provides the 2022 China Urban Ecological Risk Assessment Index and constructs a four-dimensional risk assessment framework of \"Hazard Exposure Vulnerability Water Resource Constraint (H-E-V-W)\" that integrates water resource constraints. The data covers 16 secondary indicators including climate, land use, vegetation, water resources, and socio-economic factors, all sourced from authoritative public databases such as remote sensing products, climate reanalysis data, land cover data, and statistical yearbooks. By normalizing and combining Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to calculate comprehensive weights, two types of urban ecological risk indices were finally formed: Urban Ecological Risk Index without Water Resource Constraints (UERHEV) and Urban Ecological Risk Index with Water Resource Constraints (UERHEVW). After normalizing the risk indices, they were divided into five risk levels using the equidistant method (0-0.2 for low risk, 0.2-0.4 for low risk, 0.4-0.6 for medium risk, 0.6-0.8 for high risk, and 0.8-1.0 for high risk).
", "ds_time_res": "", "ds_acq_place": "China", "ds_space_res": "", "ds_projection": "", "ds_process_way": "Unified projection transformation, spatial cropping, and normalization were performed on all raw data, and ecological risk assessment indicators and evaluation results were aggregated and calculated at the city scale according to the city boundary. To ensure the scientific and objective weighting of indicators, a combination of Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) is used to determine the comprehensive weights of each indicator, effectively avoiding bias caused by a single weight method.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "ds_topic_tags": [ "水资源约束", "城市生态风险", "危险性-暴露度-脆弱性-水资源" ], "ds_subject_tags": [ "地理学其他学科" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2022 ], "ds_contributors": [ { "true_name": "贾子续", "email": "zxjia@iue.ac.cn", "work_for": "中国科学院城市环境研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "贾子续", "email": "zxjia@iue.ac.cn", "work_for": "中国科学院城市环境研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "贾子续", "email": "zxjia@iue.ac.cn", "work_for": "中国科学院城市环境研究所", "country": "中国" } ], "category": "生态" }