Search results

This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose of this paper is to advance the comprehension of the role that geographic proximity plays in relation to non-spatial proximity in the context of international university-industry knowledge transfer.

The paper is designed as a multiple-case study. It looks at selected instances of contract research at Tallinn University of Technology that represents a typical technical university in Central and Eastern Europe characterised by relatively short period of market economy and university-industry cooperation.

The results indicate that there emerge different configurations of proximity nationally and internationally. In case of domestic cooperation cognitive (education), organisational, social and institutional (institutional setting) proximity exist simultaneously with geographic proximity. International cooperation is characterised by lack of geographical proximity, but the existence of cognitive and social proximity indicating a substitution.

The research is limited to analysing instances of contract research and relations between spatial and non-spatial forms of proximity. Further research could consider the differences between various channels of knowledge transfer and address the relationship between non-spatial forms of proximity.

The paper contributes to the existing body of knowledge by using proximity dimensions operationalised at aggregate and individual levels to study the university knowledge network. It is proposed in this paper that attention has to be paid to distinguishing between organisational and individual levels of analysis and their differing results. Proximity at organisational level does not necessarily translate into proximity between individuals and vice versa.

A burgeoning body of evidence points out the importance of spatial proximity in influencing firm efficiency besides internal characteristics. Nevertheless, the family status of the firm has been traditionally overlooked in that debate. Therefore, this study aims to investigate productivity spillovers stemming from the geographical closeness to innovators and family firms.

Using secondary data on Italian technology-intensive manufacturing firms, the paper exploits spatial econometric models to estimate productivity spillovers across firms.

As regards the presence of spatial dependence, this study reveals that a firm's level of efficiency and productivity is influenced by that of nearby firms. Specifically, three main results emerge. First, spatial proximity to innovators is beneficial for the productivity of neighbouring firms. Second, closeness to family firms is a source of negative externalities for spatially proximate firms. However, and this is the third result, the adverse effect vanishes when the nearby family firms are also innovators.

As the study relies on cross-sectional data, future research should explore productivity spillovers in a longitudinal setting. Additionally, the channels through which productivity spillovers occur should be measured.

The study highlights the importance of co-location for public policy initiatives to strengthen the competitiveness of firms and, indirectly, that of localities and regions. Moreover, the findings show the crucial role of innovation in mitigating the productivity gap between family and non-family firms.

Notwithstanding the advent of the digital era, spatial proximity and localized social relationships are still a relevant factor affecting firms' performance.

By exploring the role of family firms in influencing the advantages of geographical proximity, this study contributes to the growing efforts to explore family enterprises across spatial settings.

This paper aims to use the proximity approach of economic geography with its spatial dimension (geographic) and their non-spatial dimensions (social, institutional, cognitive and organizational) to shed light on the determinants of business cooperation with other organizations. It is also examined whetherthis cooperation is a determining factor for business innovation (innovation networks), drawing a distinction between technological and non-technological innovations.

The study has a quantitative approach; it analyzes the case of 312 companies in a cluster of agribusinesses in an emerging economy (Chile). The proposal model and its interrelations are tested with exploratory factor analysis, confirmatory factor analysis and structural equation modeling.

The results show that cognitive-organizational proximity is a positive determinant of business cooperation with other organizations, whereas social and institutional proximity are negative determinants. It is also established that business cooperation is a positive determinant of business innovation. It is more relevant in the case of technological innovation unlike non-technological innovations. In addition, it is noted that business cooperation levels are lower in micro-enterprises, a result that differs from developed countries.

For business managers, it is best to cooperate with companies that are similar in terms of cognitive and organizational levels for innovation. At the same time, it is necessary develop strategies to reduce the social and institutional barriers to cooperation, especially in the agribusiness sector.

The contributions of the study are as follows: an in-depth quantitative examination of the relationships of various non-spatial proximities as determinants of business cooperation; an analysis of whether business cooperation with other organizations is a determining factor for business innovation, distinguishing between technological and non-technological innovation; and testing these relationships in the context of agribusiness in an emerging economy such as Chile’s because most of studies are related to high-tech sector and developed economies.

The spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.

In this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.

The effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.

Traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

This chapter seeks to quantify the effects of geographic access to community health centers on the likelihood of an individual having a regular source of health care.

Utilizing survey and center location data, the analysis employs bivariate cross-tabulation with chi-square and multinominal logistic regression to quantify the relationship between variables.

While individuals living in close spatial proximity to community health centers were more likely to identify a community health center as a regular source of care as compared with those without proximal access, the effect of community health center access on the identification of any source of regular health care was generally insignificant or negative, except for populations with a chronic medical condition.

While these findings support current literature suggesting that spatial proximity to care is insufficient to transform at-risk populations into regular primary care users, it is important to note that it is possible that individuals prefer to access primary care services outside of their immediate neighborhoods, potentially mediating the observed effect of proximity to care on the likelihood of having a regular source of care. Also, because this analysis is based on cross-sectional survey data, it is impossible to make a causal argument about the relationship between variables. Only the observed association can be asserted and used to inform future studies.

Existing research supports a positive association between community health center utilization and measures of health for social groups traditionally facing barriers to care, but few studies isolate the effect of center availability and health, particularly when considering those living in the catchment area but are not regular users. Due to the complexity and prevalence of barriers to health care for vulnerable and at-risk populations, these findings suggest that improving geographic access to primary health care does not guarantee positive outcomes for target groups. The magnitude of social disadvantage on vulnerable and at-risk populations can have a devastating effect on health care outcomes that is not easily overcome by social programs.

The purpose of this paper is to address the strategies adopted by science-based start-ups to gain access to knowledge resources at diverse spatial levels. It investigates the presence and relative importance of ties endowed with different types of proximity in firms’ knowledge networks, and the role played by non-geographical proximity in gaining access to knowledge sources, both nearby and distant.

An analytical framework is proposed that distinguishes between two dimensions of proximity – geographical and relational – leading to different forms of proximity, which are further linked with modes of knowledge interaction (formal or informal). A methodology for network reconstruction is developed and applied to Portuguese molecular biology firms, permitting to identify the origin, location and nature of the ties and to position them along forms of proximity.

The results show that the incidence and mix of the different forms of proximity vary in firms’ individual networks, being possible to identify several patterns. They also uncover the relevance of relational proximity, whether or not coexisting with geographical proximity and often compensating for its absence.

This approach needs to be complemented with further research that refines the operationalization of relational proximity, by attempting to disentangle the influence of different types of non-geographical proximity. Further research will also explore in greater detail the factors that may explain variety in the proximity mix of firms’ networks.

The paper offers insights into the knowledge sourcing strategies adopted by science-based firms located outside the main concentrations of knowledge in their field.

The paper contributes to the literature on the role of proximity in knowledge access, by developing and empirically testing a taxonomy of forms of proximity, considering the characteristics of science-based firms. It uncovers the mechanisms through which relational proximity can contribute to span spatial boundaries, highlighting the role played by entrepreneurs’ personal networks.

In large organizations, space planning relies on workgroup leaders to indicate spatial adjacency preferences. However, many factors affect workgroups’ adjacency preferences, and it is not clear how the choices are made. This paper aims to explore whether the adjacency preferences are influenced by the collaboration relationship or constrained by the organizational structure.

The authors studied a large company’s spatial adjacency planning with an in-depth analysis of its formal organizational structure and collaboration network. A sample of 183 managers was surveyed regarding groups with whom they want to be spatially adjacent and groups with whom they mostly interact. The data enabled us to test three structural factors related to adjacency preference: department affiliation, workgroup’s prestige and collaboration relation. The authors used the quadratic assignment procedure analysis to examine the correlations between network matrices.

The results suggest that department affiliation and collaboration relations are significantly correlated to adjacency preferences. The authors did not find evidence supporting the notion that a workgroup’s prestige affects the preference. Among the three factors, collaboration relation best predicts the preference, which echoes Pena et al.’s (1977) argument that space planners should look into how groups function, rather than merely following the organizational chart.

To the best of the authors’ knowledge, this research was the first to explore the choice of spatially adjacent workgroup through a detailed network analysis of the formal structure, work collaboration relations and other group-level characteristics. The findings have noteworthy cross-disciplinary implications, given that spatial proximity can be taken as a human resource management strategy to facilitate the overall interactions between workgroups.

This study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.

In this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.

The results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.

The current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.