Extracting knowledge from legacy maps to delineate eco-geographical regions

ABSTRACT

Legacy ecoregion maps contain knowledge on relationships between eco-region units and their environmental factors. This study proposes a method to extract knowledge from legacy area-class maps to formulate a set of fuzzy membership functions useful for regionalization. We develop a buffer zone approach to reduce the uncertainty of boundaries between eco-region units on area-class maps. We generate buffer zones with a Euclidean distance perpendicular to the boundaries, then the original eco-region units without buffer zones serve as the basic units to generate the probability density functions (PDF) of environmental variables. Then, we transform the PDFs to fuzzy membership functions for class-zones on the map. We demonstrate the proposed method with a climatic zone map of China. The results showed that the buffer zone approach effectively reduced the uncertainties of boundaries. A buffer distance of 10–15 km was recommended in this study. The climatic zone map generated based on the extracted fuzzy membership functions showed a higher spatial stratification heterogeneity (compared to the original map). Based on the fuzzy membership functions with climate data of 1961–2015, we also prepared an updated climatic zone map. This study demonstrates the prospects of using fuzzy membership functions to delineate area classes for regionalization purpose.

KEYWORDS:

• Knowledge extraction
• ecological regionalization
• legacy map
• buffer zone
• fuzzy membership function

Data and codes availability statement

The data and codes that support the findings of this study are available in [‘figshare.com’] with the identifier at the public link (https://doi.org/10.6084/m9.figshare.c.4833234).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41530749,41971054]; Open grant from Key Laboratory of Land Surface Pattern and Simulation, CAS [LBKF201506].

Notes on contributors

Lin Yang

Lin Yang received the Ph.D degree in Cartography and Geographical Information System from Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. She is currently an associate professor in School of Geography and Ocean Science, Nanjing University. Her research interests focus on spatial sampling, spatial prediction, and data mining.

Xinming Li

Xinming Li received the Master’s degree in Cartography and Geography Information System from University of Chinese Academy of Sciences. His research interests are automated geoscience calculation and spatial analysis.

Qinye Yang

Qinye Yang is currently a researcher in the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests focus on regionalization.

Lei Zhang

Lei Zhang is currently a Ph.D. candidate at Nanjing University. His research interests include GIScience, spatio-temporal analytics, machine learning, and spatial predictive mapping.

Shujie Zhang

Shujie Zhang is currently a senior engineer at China Academy of Urban Planning & Design, and her interests are the application of big data mining and spatial analysis in urban planning.

Shaohong Wu

Shaohong Wu is currently a professor in the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests include climate change impact and adaptation, natural disaster risk, land surface pattern and process, and regionalization.

Chenghu Zhou

Chenghu Zhou is an academician of the Chinese Academy of Sciences, and currently a professor in the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. His research interests are broadly situated in research on the application of GIS and remote sensing.