%0 Dataset
%T Dataset of Rural Ecotourism Competitiveness in the Upper Reaches of the Yangtze River Economic Belt (2020)
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/454b95c0-3863-45f7-80d1-ef1e63832a5a
%W NCDC
%R 10.12072/ncdc.tourism.db7302.2026
%A dong jun
%K Upstream of the Yangtze River Economic Belt;Ecotourism Competitiveness;Rural Tourism
%X Rural eco-tourism competitiveness means that rural areas rely on a good ecological environment and use local natural landscapes and cultural landscapes as core resources. They can effectively identify the development level and potential advantages of rural eco-tourism and reveal differences between towns and villages in various aspects. Its construction can provide data support for the spatialization, comprehensive and balanced development of regional rural eco-tourism and the implementation of rural revitalization strategies. Based on 16 evaluation factors in four dimensions: natural environment, ecological resources, infrastructure and humanistic economy, the study constructed a data set on rural eco-tourism competitiveness at the township scale, and used random forest model and SHAP interpretable machine learning method to measure competitiveness., obtain its spatial distribution results. Time range: 2020, spatial range: upper reaches of the Yangtze River Economic Belt, time scale: year, spatial resolution: 30m×30m, data format: GeoTiff. This dataset integrates multi-source remote sensing, Internet and government statistical data, and the indicator coverage is relatively comprehensive. Using machine learning methods to carry out data-driven measurement can reduce the subjectivity caused by human empowerment to a certain extent; it also has model explanatory and spatial expression capabilities, which can provide a basic basis for the identification of rural eco-tourism resources in the upper reaches of the Yangtze River Economic Belt, zoning policies and cross-regional collaborative planning.