Measuring spatial co-agglomeration patterns by extending ESDA techniques

Rusche, Karsten; Kies, Uwe; Schulte, Andreas

doi:10.1007/s10037-011-0051-0

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…Lu et al (2022) examine the spatial correlation between urban form centrality and land surface temperature (LST) by investigating 27 cases of Chinese megacities [ 42 ]. Rusche et al (2011) adopted the model for the purpose of a bidimensional spatial analysis of the regional association between two German wood-based industries [ 47 ]. Loughnan et al (2008) used the bivariate global Moran’s I to determine the strength and direction of the relationship between the standardized incidence ratio and the population’s age distribution in each local statistical area [ 48 ].…”

Section: Index Development Data Sources and Methodsmentioning

confidence: 99%

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration

Liu

Chen

2022

IJERPH

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The consensus that the digital economy drives urban-rural integration has been gradually reached both in practice and theory. Besides, the way by which the digital economy drives urban-rural integration remains updated iteratively. The coming period is an important opportunity to break down the dualistic urban-rural structure and improve the urban-rural integration development. It is also a critical stage for China to promote the deep integration of the digital economy and the real economy. In this study, the intrinsic mechanism of the digital economy in driving the four dimensions of urban-rural integration was elaborated. An analysis was made of the spatial effects in 30 provinces (municipalities and autonomous regions) of China during 2011–2019 using Bivariate Global Moran’s I and geographically and temporally weighted regression (GTWR) models. As revealed by the results: (1) the digital economy and the four dimensions of urban-rural integration advance steadily, in which the convergence degree of urban and rural resident consumption is comparatively higher; (2) there is a significant spatial auto-correlation between the digital economy and the four dimensions of urban-rural integration, with the influence gradually strengthened with time; (3) the digital economy exerts mainly positive impacts on the equivalent allocation of urban and rural factors, integration of three industries in urban and rural areas, and convergence degree of urban and rural resident consumption, but inhibits the equalization of urban and rural public services in nearly half research areas; (4) both digital equipment basis and user basis play a vital role in promoting the four dimensions of urban-rural integration.

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Section: Index Development Data Sources and Methodsmentioning

confidence: 99%

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration

Liu

Chen

2022

IJERPH

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“…where d kq is a distance between establishments k or residents in block i to their nearest public space q, and g k is the number of employees in establishment k or residents in block i. These two variables together are the inputs of our bivariate function in Equation 2 (Rusche, Kies, and Schulte 2011). Bivariate Moran's I for scarcity of publicly owned public space and provision of privately owned public spaces.…”

Section: Analytical Planmentioning

confidence: 99%

“…Tourism spots, such as restaurants or parks, are excluded as they also offer the similar opportunities for refreshment and interaction with people, as public spaces do. With this set of values, the bivariate local Moran's I is calculated using Equation 4 (Rusche, Kies, and Schulte 2011). Bivariate Moran's I for public space demand and provision.…”

Section: Public Space Demandmentioning

confidence: 99%

Are They Well Situated? Spatial Analysis of Privately Owned Public Space, Manhattan, New York City

Yoon

Srinivasan

2014

Urban Affairs Review

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In this article, we attempt to examine how privately owned public spaces through incentive zoning have played out as a means to generate public spaces, as part of improving spatial equity in Manhattan, New York City. Using spatial statistical analyses, we find that the inclusion of privately owned public spaces reduces overall average distance to the nearest public space from both working and living population, and helps to balance the distribution of the public spaces throughout the city, by covering the locations where publicly owned public spaces are sparse. More importantly, the location of privately owned public spaces closely aligns with potential demand from the working and tourist population, though not from the resident population. We extend the findings to policy, suggesting that allocation of privately owned public space should be implemented comprehensively rather than discretely, as well as in relationship to publicly owned public spaces, to sharpen their role in achieving fair distribution of public spaces throughout the city.

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“…In 1997, Ellison and Glaeser discovered that co-agglomeration exists among industries with upstream and downstream division of labor [1] . The subsequent researches were mainly carried out from two aspects: the common agglomeration measure of industry and the formation mechanism of common agglomeration [2][3][4][5][6] .At present, most researches take the agglomeration among industries as the object of study, and empirical research on the co-agglomeration of financial services industry and manufacturing industry is rather rare; a few studies on the co-agglomeration among secondary and tertiary industries, manufacturing industry and production services industry were based on the provincial or prefecture city level, and studies on county spatial level is rare. In view of this, the paper, by taking E-G modification indices for reference, analyzes the levels of agglomeration of financial services industry and manufacturing and co-agglomeration between these two in Zhejiang province, and makes attempt to explain its trend and formation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Research on Spatial Co-Agglomeration Between Financial Services Industry and Manufacturing in Zhejiang Province

Zheng¹,

Zhang²

2019

Proceedings of the 2019 International Conference on Economic Management and Cultural Industry (ICEMCI 2019)

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Based on the actual data of county in Zhejiang province, this paper analyses the levels of agglomeration of financial services industry and co-agglomeration between financial services industry and manufacturing in Zhejiang province. The conclusions are as follows: The levels of financial services industry agglomeration is low in past and currently belonging to moderate agglomeration, and was inclined "N"-type upward trend; the levels of co-agglomeration are high between financial services industry and manufacturing. From the geographical point of view, northeastern of Zhejiang province was significantly higher than that in southwestern of Zhejiang province.

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Measuring spatial co-agglomeration patterns by extending ESDA techniques

Cited by 7 publications

References 31 publications

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration

Are They Well Situated? Spatial Analysis of Privately Owned Public Space, Manhattan, New York City

Research on Spatial Co-Agglomeration Between Financial Services Industry and Manufacturing in Zhejiang Province

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