本研究基于长江下游地区2000年、2005年、2010年、2015年和2020年连续的人口密度空间数据，并分析空间演变特征，为分析长江下游地区承灾体变化奠定基础。", "ds_source": "
土地利用数据来自中国科学院资源环境科学数据中心（https://www.resdc.cn/），中国人口空间分布公里网格数据集分辨率为1km。 数据集反映了人口在全国范围内的详细空间分布状况。该数据为栅格数据类型，每个栅格代表该网格范围（1平方公里）内的人口数，单位为人/平方公里，数据格式为gird。\n
中国人口空间分布公里网格数据处理过程中，首先计算土地利用类型、夜间灯光亮度、居民点密度的人口分布权重，进而在对上述3方面影响权重标准化处理的基础上计算各县级行政单元的总权重，然后在计算各县级行政单元单位权重人口占比的基础上，运用栅格空间计算，把单位权重上的人口数与总权重分布图相结合，进行人口的空间化。", "ds_process_way": "
基于ArcGIS“按掩膜提取”功能，输入研究区矢量面文件和人口分布栅格文件，得到所研究长江下游地区人口分布数据。", "ds_quality": "
长江下游地区人口主要集中在长江沿线、太湖流域，其中以上海、苏州、无锡、常州分布最为集中，面积较广；此外，南京市及其周边区域、合肥市的人口密度也较大，而安徽省内的安庆市、池州市、宣城市等城市人口密度相对较小。由此可见长江下游三角洲地区人口密度较大，从另一侧面可以反应出该区域城镇化发展较快，城区面积范围较广。\n
从时间进程来看，长江下游地区人口密度空间分布呈现越来越集中的趋势，城市中心区人口密度越来越高，人口集中在城区；城市周围乡镇及农村人口密度下降趋势明显，这与长江下游地区近20年的城镇化快速发展有着密切关系。", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "长江下游地区", "ds_acq_lon_east": 121.97, "ds_acq_lat_south": 29.59, "ds_acq_lon_west": 115.47, "ds_acq_lat_north": 32.74, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 246769590, "ds_files_count": 6, "ds_format": "*.tif", "ds_space_res": "1km", "ds_time_res": "5年", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "7131cd5e-3aa6-4e8d-b6d0-dab80ac7fdc3.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "37eb642a-c117-47e4-a677-07ecffb4b8b7", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2025-02-27 18:55:28", "last_updated": "2025-06-30 11:40:09", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NHRI.DB6768.2025", "i18n": { "en": { "title": "Dataset on spatial distribution of population density in the lower reaches of the Yangtze River (2000-2020)", "ds_format": "*.tif", "ds_source": "
Land use data are from the Resource and Environment Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/), and the spatial distribution of China's population in kilometers grid dataset has a resolution of 1km. The dataset reflects the detailed spatial distribution of the population across the country. The data is of raster data type, and each raster represents the number of population within the grid range (1 km2), the unit is person/km2, and the data format is gird. \n
China's spatial distribution of population kilometer grids in the process of data processing, firstly, to calculate the weights of the distribution of the population of the type of land use, the nighttime light brightness, and the density of the settlements, and then on the basis of standardized processing of the influence weights of the above three aspects, to calculate the weights of the distribution of the population. The total weight of each county-level administrative unit is calculated on the basis of standardized processing of the above three influencing weights, and then on the basis of calculating the proportion of the population in the unit weights of each county-level administrative unit, raster spatial calculations are applied to spatialize the population by combining the population number on the unit weights with the total weight distribution map.", "ds_quality": "
The population of the lower reaches of the Yangtze River is mainly concentrated along the Yangtze River and the Taihu Lake Basin, in which Shanghai, Suzhou, Wuxi and Changzhou are the most concentrated in the distribution of a wider area; in addition, the population density of Nanjing and its surrounding areas, Hefei City, and the population density of Anqing City, Chizhou City, Xuancheng City, and other cities within the province of Anhui, which have a relatively small population density. This shows that the population density is larger in the lower Yangtze River Delta region, which from another side can reflect the faster urbanization development in the region and the wider range of urban areas.\n
From the time process, the spatial distribution of population density in the lower reaches of the Yangtze River shows a trend of increasing concentration, with the population density in urban centers getting higher and higher, and the population concentrating in the urban areas; the population density in townships and rural areas around the cities is decreasing significantly, which has a close relationship with the rapid development of the urbanization of the lower reaches of the Yangtze River in the past 20 years.", "ds_ref_way": "", "ds_abstract": "
This study is based on the continuous spatial data of population density in 2000, 2005, 2010, 2015 and 2020 in the lower reaches of the Yangtze River and analyzes the spatial evolution characteristics, which lays the foundation for analyzing the change of disaster-bearing bodies in the lower reaches of the Yangtze River.
", "ds_time_res": "5年", "ds_acq_place": "Lower Yangtze River region", "ds_space_res": "1km", "ds_projection": "", "ds_process_way": "Based on the function of ArcGIS “Extract by Mask”, we input the vector surface file and population distribution raster file of the study area to get the population distribution data of the lower reaches of the Yangtze River under study.", "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, "is_paper_in_submitting": false, "ds_topic_tags": [ "人口密度", "空间分布", "长江下游地区" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国", "长江下游" ], "ds_time_tags": [ 2000, 2005, 2010, 2015, 2020 ], "ds_contributors": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "商守卫", "email": "ssw971216@163.com", "work_for": "南京水利科学研究院", "country": "中国" } ], "category": "生态" }