{'fraction_non_alphanumeric': 0.037003197807217905, 'fraction_numerical': 0.043398812243033345, 'mean_word_length': 4.855614973262032, 'pattern_counts': {'":': 0, '<': 1, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Regional Agglomeration Characteristics and Spatiotemporal Evolution of Snow and Ice Sports Industry in Heilongjiang Province from the Perspective of Time and Space

There are great differences in the regional development of the ice and snow sports industry in China. Most scholars only pay attention to the labor-related data while ignoring the regional spatial differences when calculating the industrial agglomeration level. This study takes the development of the ice and snow sports industry in Heilongjiang Province as the research object, studies and analyzes the agglomeration characteristics and spatiotemporal evolution characteristics of the ice and snow sports industry in Heilongjiang Province, and takes the location quotient as the response index to optimize the agglomeration and spatial layout of the ice and snow sports industry in order to obtain the optimal scheme for ice and snow sports industrial agglomeration and spatial layout. The results show that the spatial Gini coefficient (SGC) has increased from 0.043 in 2005 to 0.062 in 2019, but the overall trend shows periodic agglomeration and relative dispersion. More than 90% of the ice and snow sports output is concentrated in Harbin, Qiqihar, and other four cities, and the industrial geographical concentration (IGC4) is as high as 94.90%; the location quotient (LQ) gradually increased with time. After 2010, the industrial agglomeration level of Harbin surpassed Qiqihar and always ranked first (LQmax = 3.412). From 2012 to 2019, the spatial correlation of industrial agglomeration continuously increased (
 
 P
 <
 0.01
 
 ), and Moran’s I index showed a trend of first decreasing and then increasing with time. The response surface model for optimizing the agglomeration and spatial layout of the ice and snow sports industry is R = 2.58 + 0.42A + 0.06B−0.17C + 0.17AB−0.03AC + 0.035BD + 0.28A2−0.2B2−0.28C2−0.26D2, and the optimal scheme is as follows: when the industrial ecological efficiency (IEE) is 0.9, the ratio of tertiary industry to primary industry (PI/TI) is 2.37, industrial pollution index (IPI) is 14.68, energy consumption index (ECI) is 0.57, and LQ can reach the maximum value of 3.386.