数据内容涵盖北极国家1980-2020年人口、GDP、健康状况、农业、公共医疗、高等教育、资源消耗、对外贸易等65项社会经济指标（部分指标1980s数据缺失），以及北极地区28个行政单元1980-2020年的人口数量、人口密度、城乡人口、城镇化率、死亡率、出生率、就业人口、自然增长率、总和生育率等人口数据，并同时包括耕地面积、GDP、人均GDP等社会经济数据（部分指标1980s数据缺失）。较同类数据，其优势在于融合社会、经济、健康、环境、教育、性别平等、贸易等多维度信息，覆盖领域全面；其次，空间尺度兼具省级行政尺度与国家级尺度，满足不同研究视角需求；同时，数据时间序列完整（1980-2020 年）、指标体系丰富，可支撑北极地区社会经济动态分析、跨国对比研究、可持续发展评估等多类研究场景。", "ds_source": "
人口相关数据：包括人口数量、密度、城乡人口、出生率、死亡率、自然增长率、就业人口及总和生育率等。原始数据主要来源于北极各国官方统计机构发布的年度人口统计报告。其原始数据精度为市级/省级行政单元尺度，适用于国家级及次国家级的人口与社会发展研究。\n
经济与土地数据：包括GDP、人均GDP及耕地面积等。原始数据主要整合自北极地区各国的国民经济核算报告、区域经济账户，以及世界银行、北欧部长理事会等国际组织的数据库。其原始数据精度与人口数据相匹配，多为行政区级（省域）统计单元，适用于区域宏观经济趋势及土地利用变化分析。\n
数据融合与处理：对以上不同来源的数据进行了单位标准化、行政区划边界匹配与时间序列插补等处理，以确保多源数据在28个行政单元内（1980-2020年）的可比性与一致性。最终生成的数据产品适用于北极地区社会经济动态的综合性与对比性研究。", "ds_process_way": "
社会经济数据集加工以属性数据处理为主，属性数据（人口、GDP、出生率等表格数据）使用各国统计数据中的区域数据。数学运算方面，缺失值填补采用明尼苏达人口中心（MPC）提出的线性插值法，结合相邻年份数据估算非普查年数值；城镇化率通过 “城乡人口 / 总人口 ×100%” 公式计算，人均 GDP 按 “GDP / 总人口” 推导；数据一致性校验采用 SPSS 的 Kappa 系数检验算法（出处：《统计学方法与应用》），Kappa 值≥0.85 即判定数据一致。", "ds_quality": "
数据集加工后精度达标，可满足科研与规划需求：质量控制贯穿数据产生与汇集全流程：数据采集阶段，优先选取北极八国统计局、USGS、ESA 等权威来源，剔除非官方或未校验数据，同时记录数据来源、采集时间等元信息，确保可追溯；对文本数据采用 NLTK 工具包关键词匹配校验，人工二次核对极值数据（如异常 GDP 波动、人口骤变值）；缺失值处理后，与 MPC 数据集、FAO 土地数据库交叉比对，确保估算值偏差在合理范围；空间关联阶段，通过 ArcGIS 10.8 的拓扑检查功能修正行政边界匹配误差，基于 WGS84 坐标系统一空间基准；最终校验阶段，采用 SPSS Kappa 系数检验（Kappa 值≥0.85）验证不同来源数据一致性，排除逻辑矛盾值，形成质量控制报告存档，保障数据可靠。", "ds_acq_start_time": "1980-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "北极,加拿大,丹麦,芬兰,冰岛,挪威,瑞典,美国,俄罗斯", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 60.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 298409, "ds_files_count": 2, "ds_format": "*.xlsx", "ds_space_res": "", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "63be789f-0813-4ab9-9084-5d0ca4aa18cd.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4520" ], "quality_level": 3, "publish_time": "2026-05-07 16:13:06", "last_updated": "2026-05-07 16:14:10", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7138.2026", "i18n": { "en": { "title": "Socio economic Dataset of Arctic Provinces from 1980 to 2020", "ds_format": "*.xlsx", "ds_source": "
Population related data: including population size, density, urban-rural population, birth rate, mortality rate, natural growth rate, employed population, and total fertility rate. The raw data mainly comes from the annual population statistics reports released by official statistical agencies of Arctic countries. Its original data accuracy is at the level of municipal/provincial administrative units, suitable for population and social development research at the national and sub national levels.\r\n
Economic and land data: including GDP, per capita GDP, and cultivated land area. The raw data is mainly integrated from the national economic accounting reports and regional economic accounts of countries in the Arctic region, as well as databases of international organizations such as the World Bank and the Nordic Council of Ministers. The accuracy of its raw data matches that of population data, and it is mostly an administrative district (provincial) statistical unit, suitable for analyzing regional macroeconomic trends and land use changes.\r\n
Data fusion and processing: Unit standardization, administrative boundary matching, and time series interpolation were performed on the data from different sources to ensure comparability and consistency of multi-source data within 28 administrative units (1980-2020). The final generated data product is suitable for comprehensive and comparative research on the socio-economic dynamics of the Arctic region.", "ds_quality": "
After processing, the accuracy of the dataset meets the standards and can meet the needs of scientific research and planning. Quality control runs through the entire process of data generation and collection. During the data collection stage, authoritative sources such as the Arctic Eight Nation Bureau of Statistics, USGS, ESA, etc. are prioritized, and unofficial or unverified data is excluded. At the same time, metadata such as data sources and collection time are recorded to ensure traceability; Use NLTK toolkit keyword matching verification for text data, and manually double check extreme value data (such as abnormal GDP fluctuations and sudden population changes); After handling missing values, cross compare with MPC dataset and FAO land database to ensure that the estimated value deviation is within a reasonable range; In the spatial correlation stage, the administrative boundary matching error is corrected through the topology check function of ArcGIS 10.8, based on the WGS84 coordinate system - spatial reference; In the final verification stage, SPSS Kappa coefficient test (Kappa value ≥ 0.85) is used to verify the consistency of data from different sources, eliminate logical contradictions, and form a quality control report for archiving to ensure data reliability.", "ds_ref_way": "", "ds_abstract": "
The data covers the population of Arctic countries from 1980 to 2020 GDP 65 socio-economic indicators, including health status, agriculture, public healthcare, higher education, resource consumption, and foreign trade (some indicators have missing data from the 1980s), as well as population data from 28 administrative units in the Arctic region from 1980 to 2020, including population size, population density, urban-rural population, urbanization rate, mortality rate, birth rate, employed population, natural growth rate, total fertility rate, and arable land area GDP Per capita GDP and other socio-economic data (some indicators were missing in the 1980s). Compared to similar data, its advantage lies in integrating multidimensional information such as society, economy, health, environment, education, gender equality, and trade, covering a comprehensive range of fields; Secondly, the spatial scale combines provincial administrative scale and national scale, meeting the needs of different research perspectives; At the same time, the data time series is complete (1980-2020) and the indicator system is rich, which can support various research scenarios such as the analysis of social and economic dynamics in the Arctic region, cross-border comparative research, and sustainable development assessment.", "ds_time_res": "", "ds_acq_place": "Arctic, Canada, Denmark, Finland, Iceland, Norway, Sweden, United States, Russia", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
The processing of socio-economic datasets mainly focuses on attribute data processing, with attribute data (such as population, GDP, birth rate, etc.) using regional data from national statistical data. In terms of mathematical operations, missing values are filled using the linear interpolation method proposed by the Minnesota Population Center (MPC), combined with adjacent year data to estimate non census year values; The urbanization rate is calculated using the formula of \"urban-rural population/total population x 100%\", and the per capita GDP is derived based on \"GDP/total population\"; The data consistency check adopts the Kappa coefficient test algorithm of SPSS (source: \"Statistical Methods and Applications\"), and a Kappa value ≥ 0.85 is considered to be consistent with the data.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "https://creativecommons.org/licenses/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": [ "总人口", "长时序", "北极", "极地地区" ], "ds_subject_tags": [ "人文地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北极", "加拿大", "丹麦", "芬兰", "冰岛", "挪威", "瑞典", "美国", "俄罗斯" ], "ds_time_tags": [ 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "王世金", "email": "wangshijin@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "王世金", "email": "wangshijin@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院冰冻圈科学与冻土工程重点实验室", "country": "中国" } ], "ds_managers": [ { "true_name": "强文丽", "email": "qiangwl@lzu.edu.cn", "work_for": "兰州大学", "country": "中国" } ], "category": "极地" }