中国城市人工智能发展的时空演化特征及其影响因素

doi:10.13249/j.cnki.sgs.2022.07.008

Abstract

Abstract:

Based on the panel data of 285 cities in China from 2000 to 2019, this paper searches the number of patent applications related to artificial intelligence with keywords. Using standard deviation ellipse, exploratory spatial data analysis and spatial Dubin decomposition model, this paper analyzes the temporal and spatial evolution characteristics and influencing factors of urban artificial intelligence in China. The results show that: 1) The development of artificial intelligence can be divided into initial stage, growth stage and high-speed development stage. There is an obvious hierarchical effect in space. The advantages of eastern coastal cities are obvious, and the rise of central and western cities are accelerated. 2) Artificial intelligence has a strong spatial positive correlation, and cities in some areas form an agglomeration development trend. The spatial distribution produces polarization along the “northeast-southwest” and diffusion along the “northwest-southeast” direction. The distribution center is located in Anhui Province, with a trend of continuous migration to the southeast. 3) The improvement of marketization level, population density, human capital, foreign investment level and financial development level are conducive to the development of artificial intelligence, and there is a positive spatial spillover effect. The relationship between government intervention and the development of artificial intelligence shows an inverted U-shaped curve, with spatial spillover effect. Industrial upgrading and infrastructure construction are conducive to the development of artificial intelligence, but the spatial spillover effect are not obvious. The research conclusion can provide empirical enlightenment for China’s cities to accelerate the implementation of artificial intelligence strategy.

Key words: Chinese cities, artificial intelligence, standard deviation ellipse, spatial Dubin model

CLC Number:

F061.5

Zou Weiyong, Xiong Yunjun. Spatio-temproral Evolution Characteristics of AI Development in Chinese Cities and Its Influencing Factors[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(7): 1207-1217.

Figures/Tables 9

Table 1

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Table 2

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Table 4

References 30

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序号	衡量维度	检索关键词
1	算法	深度学习、机器学习、遗传算法、神经网络
2	数据	大数据、自然语言处理、机器视觉、计算机视觉
3	计算能力	专家系统、复杂系统、云计算
4	应用场景	智能机器人、工业机器人、智能制造、智能控制、自动驾驶、无人机、语音识别、图像识别、人脸识别、指纹识别、生物识别
5	相关技术	人工智能、智能技术、机器人技术、自动化技术、人机交互

年份	重心坐标	长半轴/km	短半轴/km	方位角	面积/万km²	形状指数
注：数据来源ArcGIS10.5软件测算得出；不含港澳台数据。
2000	（115°50′02″E，33°38′13″N）	1068.704	593.967	53°18′11″	199.404	0.556
2003	（116°41′46″E，32°14′38″N）	1013.212	573.025	50°47′46″	182.385	0.566
2006	（116°32′17″E，32°24′29″N）	1052.124	610.482	53°09′22″	201.770	0.580
2009	（116°32′10″E，32°36′25″N）	959.351	611.963	49°58′05″	184.426	0.638
2012	（116°17′02″E，32°05′31″N）	920.173	635.201	49°16′26″	183.613	0.690
2016	（115°57′00″E，32°01′19″N）	927.347	628.656	49°20′02″	183.138	0.678
2019	（115°55′05″E，31°15′29″N）	932.435	602.868	48°47′20″	176.588	0.647

年份	Moran’sI	Z值	P值	年份	Moran’s I	Z值	P值
注：不含港澳台数据。
2000	0.071	3.788	0.000	2010	0.226	11.267	0.000
2001	0.057	3.010	0.001	2011	0.232	11.569	0.000
2002	0.107	5.499	0.000	2012	0.241	11.987	0.000
2003	0.101	5.185	0.000	2013	0.249	12.403	0.000
2004	0.138	7.011	0.000	2014	0.253	12.584	0.000
2005	0.143	7.273	0.000	2015	0.252	12.533	0.000
2006	0.160	8.106	0.000	2016	0.247	12.292	0.000
2007	0.156	7.892	0.000	2017	0.260	12.916	0.000
2008	0.196	9.858	0.000	2018	0.264	13.154	0.000
2009	0.226	11.302	0.000	2019	0.255	12.718	0.000

变量	主效应项	空间滞后项	直接效应	间接效应	总效应
注：括号内数值为各系数的标准误差；^*、^、^分别表示1%，5%，10%水平的显著性； lnmar为市场化水平； lngov为政府干预； lnpop为人口密度；lnhum为人力资本；lnfdi为外商投资；lnfin为金融发展；lnind为产业升级；lninst为基础设施建设；SDM-SAR为SDM模型是否会退化为SAR模型的LR检验；SDM-SEM为SDM模型是否会退化为SEM模型的LR检验；Both-Ind为时空双固定模型还是空间固定模型更优的LR检验；Both-Time*为时空双固定模型还是时间固定模型更优的LR检验；不含港澳台数据。
lnmar	0.016	0.394^***	0.032^*	0.846^***	0.878^***
lnmar	(0.018)	(0.068)	(0.019)	(0.151)	(0.153)
lngov	0.247^***	0.261	0.260^***	0.806^**	1.066^***
lngov	(0.054)	(0.189)	(0.051)	(0.374)	(0.376)
lngov²	−0.097^***	−0.225^***	−0.106^***	−0.571^***	−0.677^***
lngov²	(0.012)	(0.040)	(0.011)	(0.081)	(0.079)
lnpop	0.561^***	1.398^***	0.625^***	3.568^***	4.193^***
lnpop	(0.110)	(0.464)	(0.109)	(0.975)	(0.999)
lnhum	0.068^**	0.837^***	0.102^***	1.810^***	1.912^***
lnhum	(0.030)	(0.113)	(0.030)	(0.247)	(0.251)
lnfdi	0.022	0.184^***	0.029^*	0.408^***	0.437^***
lnfdi	(0.015)	(0.056)	(0.016)	(0.120)	(0.123)
lnfin	0.236^***	0.822^***	0.269^***	1.987^***	2.255^***
lnfin	(0.039)	(0.148)	(0.038)	(0.314)	(0.320)
lnind	0.207^***	0.284	0.228^***	0.774	1.002^*
lnind	(0.075)	(0.284)	(0.073)	(0.583)	(0.590)
lninst	0.204^***	0.096	0.210^***	0.405	0.616^**
lninst	(0.043)	(0.153)	(0.041)	(0.302)	(0.304)
ρ	0.526^***
sigma2_e	0.367^***
HausmanTest	811.020^***
SDM-SAR	213.880^***
SDM-SEM	339.800^***
Both-Ind	139.370^***
Both-Time	2655.250^***
R-squared	0.287
观测值	5700
样本数	285

Spatio-temproral Evolution Characteristics of AI Development in Chinese Cities and Its Influencing Factors

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