本文基于1934—2019年《地理学报》期刊发表的154篇工业地理学论文分析中国工业地理发展脉络与主要领域研究进展。中国工业地理在融合了西方工业地理理论和苏联工业地理理论基础上,结合中国实际情况逐渐发展出兼顾理论研究和实践双重任务驱动下的特色学科体系,在经历了诞生探索、成长总结、视角转换、稳步发展4个阶段后,学科内容和范式逐渐与国际接轨,成长为一门相对成熟且具有重要意义的学科。在学科建设方面,未来需要继续服务社会经济建设,增强理论创新能力,加强新技术手段应用,结合中国制度环境推动建设中国特色的工业地理学。

It is very important to summarize the research, understand the development process and direction of industrial geography in China scientifically under the context of the development of international industrial geography. Based on 154 papers on industrial geography published in Acta Geographica Sinica from 1934-2019, this paper reviewed the development process of industrial geography in China and the progress of research in major fields. This paper showed that China's industrial geography is a unique theoretical system based on the integration of Western industrial geography theory and Soviet-style industrial geography theory, under the tasks of theory development and practices. Going through exploration, growth, perspective changing and deepening, industrial geography is gradually brought into line with the international researches on the content and paradigm. The development of discipline needs to meet the national strategic demand, raise theoretical innovation capacity, strengthen the application of new methods and techniques, and develop theories of industrial geography with the Chinese institutional context.

聚焦于中国制造业内部的产业关联与空间分布,基于《中国投入产出表》中涉及的17个制造业细分行业和中国286个地级市空间单元的统计数据,研究与某一制造业细分行业具有较强产业关联的其他制造业细分行业与其空间分布之间是否存在相关性,并进一步分析产业关联强且具有空间关联的产业组合的主要特征。研究表明,在68个产业关联较强的产业组合中,有39个产业组合具有空间关联;产业关联较强的劳动/劳动密集型制造业和资本/技术密集型制造业更容易呈现空间关联;超过半数的制造业细分行业的产业关联和空间关联的程度之间存在正相关。此外,在产业特征分析基础上,利用GWR模型对39个产业关联强且具有空间关联的产业组合的空间关联度在空间分布上的特征和差异进行分析。结果显示,产业组合的空间关联度较高区域多分布在中等发展水平省区,而在经济发达或欠发达省区分布较少;东北三省区空间关联度较高的产业组合基本一致,产业发展情况较为相似。

The spatial distribution of the manufacturing industry based on industrial linkage has always been an important research topic. Using statistical data of 17 manufacturing industry segments in China (input-output table) and 286 prefecture-level spatial cells, this paper carries out input-output analysis and uses spatial regression models to determine the industrial linkage and spatial distribution of the manufacturing industry in China. Employing ordinary least squares (OLS), spatial lag regression model (SLM) and spatial error regression model (SEM), this paper determines whether there are correlations on spatial distribution between some manufacturing industry segments; it also aims to determine whether the distribution has strong industrial linkage to a specific segment. This paper also examines the characteristics of the industrial combinations with both strong industrial linkage and spatial correlation. Results show that, firstly, in 68 industrial combinations, 39 of these have both strong industrial linkage and spatial correlation, which proves the Marshallian externalities to some extent. Secondly, labor/labor-intensive manufacturing industries and capital/technology-intensive manufacturing industries can easily form such industrial combinations. Third, more than half of the manufacturing industry segments have positive correlations between industrial linkage and spatial correlation. Finally, "pgdp", "city", “kmt", and "zone" have good feedback, indicating that these elements have effects on the spatial distribution of the manufacturing industry in China. Apart from industrial combinations, this paper uses geographically weighted regression model (GWR) to study the spatial distribution of the degree of spatial correlation of 39 industrial combinations. Results show that industrial combinations with higher degree of spatial correlations are generally located in developing provinces (Heilongjiang, Jilin, Liaoning, Inner Mongolia, Shanxi, Hunan and Jiangxi), and not in developed provinces (Beijing, Tianjin, Jiangsu, Shanghai and Zhejiang) or under-developed provinces (Ningxia, Qinghai, Xinjiang and Tibet). Parts of the spatial distribution of the degree of spatial correlation of industrial combinations have regularities. Labor/labor-intensive manufacturing industries and capital/technology-intensive manufacturing industries differ in terms of the spatial distribution of the degree of spatial correlation. Moreover, the industrial combinations with higher degree of spatial correlations are basically the same in Heilongjiang, Jilin, and Liaoning. To ensure the future development of the manufacturing industry, the government should pay more attention to the mutual coordination and spatial correlation between manufacturing industry segments with strong industrial linkage. Formulating corresponding industrial linkages based on different manufacturing industry divisions and geographic spaces shall also play a positive role in the optimization of the spatial layout, further transforming and upgrading the manufacturing industry in China.

珠三角的跨境资本因其文化多元性、地缘特殊性而受到国际经济地理学家的广泛关注,“多元资本主义”等多种理论框架被提出以解释其特殊性。然而,由于数据、方法的限制,相关研究多停留于理论及定性探讨层面,基于微观企业样点的研究较为欠缺,特别是纳入企业异质性等新经济因素的定量模型研究更为缺乏。论文基于微观企业样点数据,综合考虑传统一般经济因素与制度、集聚、创新、企业异质性等新经济因素,采用核密度分析方法以及空间回归分析,研究了2005—2009、2009—2013年2个时段的珠三角新晋跨境制造业企业(new-born cross-border manufacturing firms, NCMF)的空间分布格局与差异,并重点关注金融危机前后(2005—2009年)这一特殊的经济转型时期,珠三角NCMF区位选择的影响机制。结果表明：①珠三角NCMF的空间集聚程度较以往有所降低,2005—2009年与2009—2013年2个时段的NCMF在结构与分布特征上呈现出较大的差异,热点区位在2005年后开始由珠三角东岸地区向西岸拓展,2009年后则再次呈现以东岸为主的集聚特征;②金融危机前后,珠三角的发展动力发生了根本性的变化,以往吸引跨境制造业企业的劳动力成本优势与开发区政策优势逐步弱化,而集聚因素、创新因素以及企业异质性成为影响跨境制造业企业空间集聚与区位选择的关键影响因素;③处于动力转换期的珠三角,需要通过构建完善的制度环境、建立创新网络、增强要素集聚、根据企业偏好制定多元化引资政策来重新获得发展的新优势。论文旨在从企业微观层面揭示经济区域内部的外商直接投资区位,同时也为理解转型时期中国以珠三角为代表的外向型经济区域的动力转换与空间重构提供实证参考。

The cross-border capital of the Pearl River Delta has received extensive attention from international economic geographers because of its cultural diversity and geopolitical specificity. The theoretical model of "hybrid capitalism" was proposed base on this. However, due to the limitations of data and methods, relevant research mostly stays at the level of theoretical and qualitative exploration. The research based on firm-level samples is deficient, especially quantitative models that incorporate the new economic factors such as firm heterogeneity. Based on the sample data of enterprises, this study synthetically considered the traditional economic factors and new economic factors such as institution, agglomeration, innovation, and firm heterogeneity. In order to explain the mechanism of foreign direct investment (FDI) location choice from the firm level, we also combined a spatial analysis method and spatial regression model. Kernel density spatial analysis and spatial lag modeling based on firm samples were conducted to uncover the geographical agglomeration characteristics and location factors of the new-born cross-border manufacturing firms (NCMF) in the Pearl River Delta (PRD) during 2005-2009 and 2009-2013. To explore the location choice mechanism of FDI in the transitional period, we also focused on the impact mechanism of location choice of NCMF in the PRD before and after the global financial crisis (2005-2009). The main results are as follow: 1) The spatial agglomeration of the NCMF in the PRD is lower than before. The NCMF in the two periods of 2005-2009 and 2009-2013 showed great differences in structure and distribution characteristics. The hotspot of firm distribution began to expand from the east coast of the PRD to the west side after 2005, and after 2009, firm agglomeration in the east coast was again obvious. 2) After the global financial crisis, the development momentum of the PRD has undergone fundamental changes. The advantages of low labor cost and preferential policies of development zones that attracted cross-border manufacturing firms have gradually weakened, while agglomeration factors, innovation factors, and firm heterogeneity have become the key factors that affect the spatial agglomeration and location choice of NCMF. 3) In the period of a dynamic transformation, the Pearl River Delta needs to regain its new advantages of development by building a sound institutional environment, establishing innovation networks, enhancing factor agglomeration, and formulating diversified investment policies according to firm preferences. This study examined the location choice of FDI within the economic region using the firm-level data, and may provide an empirical reference for understanding the dynamic transformation and spatial reconstruction of the export-oriented economic region represented by the Pearl River Delta in China during the transition period.

镶嵌于城市地域的都市型工业,有着其独特的区位特征和空间格局。基于2008年上海都市型工业微观企业数据,探讨工业郊区化背景下都市型工业空间分布、集聚特征以及行业间的空间邻近关系。研究表明：上海都市型工业企业区位选择具有中心城区与近郊区双重指向性,在距离城市中心10~20 km区间都市工业分布最密集,空间分布的非均质特征明显。空间集聚以城市内各类园区（商业楼宇、都市工业园、工业园区）为主要载体,集聚格局在行业间存在一定差异。在空间邻近关系方面,各行业间呈现出基于相同要素投入、生产关联、共同市场、政策导向的邻近布局以及基于异质排他性、环境要求的弱邻近布局特征。

The urban industry is embedded in the city area which has its unique location characteristics and spatial pattern. Based on the micro-data of Shanghai urban industry enterprises in 2008, with the methods of location quotient, hot spot analysis and proximity index analysis, the paper explores the spatial distribution and the agglomeration characteristics of urban industries, as well as the spatial proximity relations among various industries during the process of industrial suburbanization. Results show that: First of all, the location choice of urban industry enterprises in Shanghai has a dual directivity to both central city and suburban areas, the range of 10~20 kilometers from city center is the densest area of urban industries, and the overall spatial distribution of urban industries has obvious heterogeneous characteristics. Food processing, packaging and printing, manufacture of tourism crafts and manufacture of small-scale electronic information constitute comparative advantage industries of central city, manufacture of interior adornment composes the comparative advantage industry of suburbs, and clothing accessories industry and manufacture of makeup and washing supplies constitute comparative advantage industries of suburban regions. Secondly, all kinds of industrial parks including commercial buildings, urban industrial zones and industrial estates in the city have become the major carrier of spatial agglomeration, which present "crater" gathering morphology. There are also some differences among the inter-industry according to the pattern of spatial agglomeration. Thirdly, when it comes to the spatial proximity relations, aside from being affected by some factors including the local government development policies, economic history basis and traffic location, the layout between all urban industries shows the following characteristics: the proximity layout is based on the same factor inputs, productive correlation, common market and policy orientation while the weak proximity layout is based on the heterogeneous exclusivity and environmental requirements.

通信设备制造业是中国国民经济的重要产业。近年来,中国通信设备制造业发展迅速,其空间格局呈现出新的特征。论文基于中国2013年规模以上通信设备制造业企业数据、2014—2019年成立的通信设备制造企业数据,运用核密度分析、负二项回归分析等方法,分析中国新创通信设备制造企业空间格局及其影响因素。研究发现：① 中国新创通信设备制造企业趋向在该产业基础较好的珠三角、长三角、京津冀、成渝4大城市群集聚,其中以深圳为中心的珠三角产业集聚优势更加明显;② 在4大城市群内部,新创通信设备制造企业从核心地区向外围地区扩散;③ 该产业原有产业基础较弱的南昌市、红河州、郑州市、贵阳市等中西部地区,也集聚了一些新创通信设备制造龙头企业;④ 城市的知识复杂性和原有产业基础对所有类型新创通信设备制造企业进入具有正向的影响,产业关联密度有助于增加内资和大规模企业进入的机会,非相关多样化有利于外资企业和大规模企业进入,全球化和政府力量分别有助于外资企业、大规模企业进入。

Telecommunication equipment manufacturing is a pillar industry of the Chinese economy. Its spatial distribution has been evolving in recent years due to the rapid development of the industry. This study analyzed the spatial pattern and underlying factors of new telecommunication equipment ventures with detailed firm-level data from the National Enterprise Credit Information Publicity System and China's Annual Survey of Industrial Firms, as well as analytical methods including kernel density analysis and negative binomial regression. The analysis points to the following key findings: 1) China's new telecommunication equipment manufacturing ventures tend to cluster in four leading city-regions—the Pearl River Delta, Yangtze River Delta, Beijing-Tianjin-Hebei Area, and Chengdu-Chongqing Area. 2) Within these four city-regions, firms in this industry have been diffusing from central cities to the peripheries. 3) A few clusters of leading firms have been observed in inland regions previously with limited bases for this industry, such as Nanchang, Honghe, Zhengzhou, and Guiyang cities. 4) Knowledge complexity and the existing industrial base of cities are positively associated with the entry of all new firms. The density of related industries is positively associated with the entry of domestic enterprises and large-scale enterprises. Unrelated variety correlates with the entry probability of foreign-invested enterprises and large-scale enterprises. Furthermore, our analysis suggests that globalization and government intervention are associated with the entry of foreign-invested and large-scale enterprises, respectively.

以微观企业数据为基础,采用核密度空间平滑和条件逻辑选择模型等方法,重点关注企业在城市内部微观尺度下的区位选择,并对北京典型现代服务业和制造业区位选择的差异进行对比分析。结果表明：① 北京中小型制造企业倾向于在集聚区分布,符合马歇尔式产业集群的特征,而服务业则是大型企业更倾向于集中布局;② 尽管微观尺度下多样化和上下游产业链对现代服务业和制造业依然有重要影响,但是与宏观尺度下不同的是对于现代服务业来说过度多样化并不利于其发展,对于制造业来说区域专业化劳动力规模而非专业化程度更为重要;③ 政府对地铁等公共设施布局选址将对现代服务业的微观区位产生重要影响。

Industrial location has attracted much attention since classical location theories were built, but most of literature focused on location choice at macro scale. However, micro-scale location choice became a troublesome problem due to the reduction of land and the increase of land price. The research employed a unique micro-firm dataset to identify industrial agglomeration areas of service and manufacturing industries in Beijing and then uncovered factors which have impact on micro-scale location choice within urban areas. Importantly, the result further showed difference of location choice between two typical economic sectors--management service and automobile manufacturing in Beijing. The findings are obtained as follows: (1) 102 service and 130 manufacturing agglomeration areas were identified by firm and employment densities. Firms within these two kinds of agglomeration areas accounted for 72.66% and 44% in service and manufacturing industries respectively. Generally, most of large service firms had concentrated into those service clusters, while large manufacturing enterprises used to be located otherwise. Compared with large firms, small and medium-sized manufacturing firms were more inclined to distribute in agglomeration areas. (2) Although the results showed that urbanization economies had significant effect on location choice of both modern service (representing by management service) and manufacturing industry (representing by automobile manufacturing), the influence mechanisms are completely different. The former prefers a diversified local labor market and relatively diversified industrial environment, while the latter needs a complete industry supplying chain. It is only because the automobile industry has a diverse and comprehensive range of auto parts, it seems that automobile manufacturing firms prefer diversified environment. Naturally, upstream and downstream industry chains and specialized labors are key factors for location choice of automobile firms. Even for the service industry, it is not the more diversified the better at the micro scale. (3) Different from existing literature, the results showed that industrial policies had more influence on service than manufacturing industry. It is partly because one of important factors for location of management service is public infrastructure such as subway, on which local governments have enough impact; and partly because, automobile, as an advanced manufacturing industry, is not among the negative list of manufacturing. Besides, enterprise property has significant influence on location choice of both industries.

零售活动的空间关系研究是城市地理学研究的一个热点问题。以上海市内环地区的星巴克、COSTA和瑞幸咖啡三家咖啡连锁公司的门店为研究对象,综合运用多种空间统计方法和实地调研分析三者之间的空间关联特征。结果表明：1）无论是传统零售还是新零售模式下的咖啡门店,其空间分布均大致表现出靠近消费市场的空间导向特征；2）星巴克门店的空间集聚程度最强,瑞幸咖啡门店空间集聚程度最弱；3）利用一种新的多元空间统计方法计算后发现,无论是传统咖啡零售星巴克和COSTA的门店,还是星巴克与新零售瑞幸咖啡的门店,均呈现显著相互吸引的空间关联特征,但其形成机制存在差异；4）星巴克与COSTA常常成对出现在商圈或商务区中心位置,而后进入市场的瑞幸咖啡门店常常位于“非中心”位置。新零售模式可以通过互联网平台、大数据分析等技术重构交易基础逻辑,改变传统零售依赖门店的成本结构模型；同时,新零售模式高度重视用户线上体验,在一定程度上弱化了实体区位的重要性。因此,在进行城市规划,特别是商业区规划时,应重视新零售模式对区位选择的影响,重视互联网与大数据在区位决策中的作用,挖掘传统弱区位地区或地段的发展可能性,提高城市土地利用效率。

Research on the spatial relationship of retail activities is a hot topic in urban geography. With the continuous upgrade of Internet technology, the development mode of business has evolved from the traditional retail model into the e-commerce model, and now the new retail model. While the new retail model influences the locational decision-making behavior of enterprises, it also affects the intrinsic mechanism of attraction and avoidance among retail stores, which in turn affects the spatial association relationship of commercial retail. In such a context, we take the Starbucks, COSTA, and Luckin Coffee stores in Shanghai's inner ring as our research objects, and use a variety of spatial statistical methods and field research to analyze the spatial correlation characteristics among the three. The results show that, first, the spatial distribution of coffee stores under both traditional and new retail models generally exhibit the spatially oriented characteristics of being close to the consumer market. Therefore, it can be indirectly inferred that although Luckin Coffee, which is characterized by new retail, can create "infinite space" to meet consumers' consumption needs in different spaces by virtue of its own Internet development advantage, it remains difficult to completely break away from the spatial orientation of the offline consumption market. Second, in terms of spatial agglomeration, Starbucks' high sensitivity to specific consumer groups and its sales strategy of providing a comfortable environment have led the business to open stores in dense proximity in locations with high consumption potential, thus contributing to its strongest spatial agglomeration. Luckin Coffee, by contrast, has a certain degree of flexibility in choosing store locations due to its independent instant delivery service, and in order to occupy a wider market as soon as possible, it chooses store locations in favor of uniform coverage, resulting in the weakest degree of spatial agglomeration. Third, based on multivariate spatial statistics, it can be seen that Starbucks, COSTA, and Luckin coffee stores all exhibit positive spatial relationship characteristics in the two corresponding spatial relationships. Among them, traditional coffee retailers Starbucks and COSTA show a more obvious spatial relationship of mutual attraction, indicating that both can increase their market shares by converting the fierce price competition between them into an attraction drive to increase their total profits. At the same time, the stores of traditional retailer Starbucks and new retailer Luckin Coffee also show a significant spatial relationship of mutual attraction in space, indicating that the market share effect dominates. Finally, micro-location analysis reveals that Starbucks and COSTA stores have a stronger mutual attraction relationship and often appear in pairs in the center of shopping districts or business areas, while Luckin Coffee stores are often located in "non-central" areas. As a representative of new retail, Luckin Coffee can make up for its location disadvantage to a certain extent by virtue of its mobile application online service and instant delivery service. The store can also utilize its Internet platform, big data analysis, and other technical advantages, so that it can combine its own product positioning characteristics when choosing store locations, and accurately find potential store locations and opening models. Therefore, when carrying out urban planning, especially the planning of commercial areas, attention should be paid to the impact of the new retail model on location selection, the role of the Internet, and big data in location decision making. The development possibilities of traditionally weaker locations should be explored, and the efficiency of urban land use should be improved.

就业地和居住地是城市居民工作以及生活的主要场所,同样也是城市空间结构最重要的组成要素,写字楼与居民楼空间关联性研究对深刻理解职住关系有着至关重要的作用。利用协同区位商方法,对北京市写字楼与居民楼空间关联总体特征和局域空间关联格局进行分析。研究表明：① 协同区位商作为一种测量点要素之间联系的方法,能够很好的应用到职住关系的研究中,对丰富职住关系度量指标体系也具有非常重要的意义。② 对北京市的实证分析结果显示,北京市写字楼与居民楼全局协同区位商值小于1,空间关联性较差,表明职住要素的空间联系总体较弱。③ 局域协同区位商的分析表明,北京写字楼与居民楼局域空间关联格局差异明显,不仅不同区域两者空间关联性强弱差别显著,而且不同价位的居民楼和写字楼展现出不同的空间关联特征。④ 北京职住要素空间关联性深受写字楼、居民楼本身布局的影响,其主要影响因素还需要进一步全面分析,加强此领域研究,将有助于城市规划中职住要素的合理空间布局。

Places of employment and residence are the main spaces in which urban residents work and live, as well as key elements of the urban spatial structure. Hence, thorough investigations of the spatial correlations of office and residential buildings are of great significance for the understanding of the spatial relationships of urban elements, especially that of job-housing relationships. In this study, the objects of the research were office and residential buildings in Beijing, China, and the global and local characteristics of the spatial relationships between job-housing elements were investigated using the collaborative location quotient method. The results demonstrate that: (1) The co-location quotient, a method used to measure the spatial correlations of point elements of a survey, can be effectively applied in studies of job-housing relationships, and contributes indicators and a method for the measurement of job-housing relationships. (2) The empirical analysis reveals that the global co-location quotient (GCLQ) of office and residential buildings in Beijing is below 1, indicating relatively weak spatial correlations of the job-housing elements in the city, which is consistent with the increasing job-housing separation. Overall, residential buildings are more attracted by office buildings, suggesting that the location selection of residential buildings is affected by the local distribution of office buildings, whereas the local distribution of office buildings is barely a consideration for the location selection of residential buildings. (3) The results of the local co-location quotient (LCLQ) demonstrate that the local relationships of office and residential buildings in Beijing vary significantly in space. As the distance from the urban center increases, the LCLQ of residential buildings to office buildings decreases, while the LCLQ of office buildings to residential buildings increases. Moreover, the spatial relationships of urban job-housing elements in the north and south areas of Beijing, the dividing line of which is Chang'an Avenue, are significantly different; the spatial correlation of office and residential buildings in the north area is relatively strong, while that in the south area is relatively weak (i.e., the distribution of residential buildings is independent of that of office buildings). Additionally, the result show that the spatial relationships of office and residential buildings are related to their prices. (4) Recently, researchers have turned from studies of the urban hierarchical structure based on theories of "the space of places" to studies of the trends of "the space of flows", namely population flow, logistics flow, and information flow. This study shares a similar logic, as it investigates the urban spatial structure from the perspective of elemental correlation. This research is of great significance for the understanding of the functional zones of living, working, recreation, and transportation in cities, and relevant studies will contribute to the reasonable spatial layout of job-housing elements in urban planning.

China’s economy has undergone rapid transition and industrial restructuring. The term “urban industry” describes a particular type of industry within Chinese cities experiencing restructuring. Given the high percentage of industrial firms that have either closed or relocated from city centres to the urban fringe and beyond, emergent global cities such as Shanghai, are implementing strategies for local economic and urban development, which involve urban industrial upgrading numerous firms in the city centre and urban fringe. This study aims to analyze the location patterns of seven urban industrial sectors within the Shanghai urban region using 2008 micro-geography data. To avoid Modi?able Areal Unit Problem (MAUP) issue, four distance-based measures including nearest neighbourhood analysis, Kernel density estimation, K-function and co-location quotient have been extensively applied to analyze and compare the concentration and co-location between the seven sectors. The results reveal disparate patterns varying with distance and interesting co-location as well. The results are as follows: the city centre and the urban fringe have the highest intensity of urban industrial firms, but the zones with 20-30 km from the city centre is a watershed for most categories; the degree of concentration varies with distance, weaker at shorter distance, increasing up to the maximum distance of 30 km and then decreasing until 50 km; for all urban industries, there are three types of patterns, mixture of clustered, random and dispersed distribution at a varied range of distances. Consequently, this paper argues that the location pattern of urban industry reflects the stage-specific industrial restructuring and spatial transformation, conditioned by sustainability objectives.