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This study aims to apply a proposed methodology for calculating spatial prices in a heterogeneous country setting such as India with limited price information. Based on the empirical evidence, the study plans to draw the spatial price map of India with different colours denoting states and districts with varying level of spatial prices.

This study shows that a procedure proposed by Lewbel (1989), based on the idea by Barten (1964) that household composition changes have “quasi-price effects”, can be used to estimate spatial prices in the absence of information on regional prices.

The evidence on spatial price differences in India, which is the most comprehensive to date because it goes down to district level, shows that the proposed procedure has considerable potential in future applications on other data sets with limited price information. The policy importance of the results is underlined by the sensitivity of the demand elasticities to the inclusion/omission of spatial price variation.

The study uses “pseudo unit values” based on household composition and demographic effects on demand as proxy for the missing price information. While the work of Atella et al. (2004) suggests that such proxies are accurate representations of true prices, nevertheless, they are proxies and the results should be treated with caution.

The evidence on spatial prices in India that point to a high degree of price heterogeneity between regions implies that welfare applications such as income distributional and poverty studies must take account of the price heterogeneity within the country. The implications extend beyond India to cross-country exercises such as the purchasing power parity calculations undertaken by the International Comparison Project.

This is one of the first studies that provide evidence on spatial price heterogeneity within a country without requiring regional price information. Methodologically, the paper builds on the suggestion of Lewbel (RES, 1989) in showing how the demographic effects on household expenditure pattern can be used to estimate spatial prices. The value of the contribution lies in the use that the estimated spatial prices can be put to in calculating inequality and poverty rates and in standard of living comparisons between regions in the country.

This paper aims to contribute to the approaches based on traditional industry concentration statistics for identifying clusters by complementing them with the techniques of exploratory spatial data analysis (ESDA).

Using a sample with 34,500 observations retrieved from the social information annual report released by Brazil Ministry of Labor and Employment, the methodology was designed to make a comparison between the application of industry concentration statistics and ESDA statistics.

As the results show, the geographic distribution measures proved to be fundamental for longitudinal studies on regional dynamics and industrial agglomerations, and the local indicator of spatial association statistic tends to overcome the limitation of the industry concentration approach.

In the period considered, due to economic, structural and circumstantial questions, activities linked to the transformation industry have been losing ground in the value creation process in Brazil. In this sense, the study of other industries may generate other types of insights that should be considered in the process of regional development.

This paper offers a critical analysis of empirical approaches and methodological advances with an emphasis on the treatment of special effects: spatial dependence, spatial heterogeneity and spatial scale. However, the regional dynamic presents a temporal dimension and a spatial dimension. The role of space has increasingly attracted attention in the analysis of economic changes. This work has identified opportunities for incorporating spatial effects in regional analysis over time.

The purpose of this paper is to develop a methodology for shaping the tourist spatial identity of the city and to take advantage of it to discover alternative urban outdoor spaces. As the number of indoor visitors has been limited due to the COVID-19 pandemic, open urban areas such as streets, squares and parks have become more important tourist locations.

The assessment methodology consists of two basic steps. In the first step, the authors look for places or points that are carriers of spatial identity. For this purpose, the method of mental mapping is used. In the second step, statistical methods are used to evaluate the spatial suitability for the most common tourist activities. To obtain a holistic picture, a temporal component is included.

The application of the methodology is presented in the form of a case study. The obtained research results provide an insight into the spatial situation of the city of Maribor (Slovenia, Europe). Tourist spatial identity of a city depends on time. Based on the value of spatial sensitivity indicator and the suitability of activities, it is possible to adapt the tourist offer to the temporal component.

To the best of the authors’ knowledge, this is an original perspective on the spatial identity of tourists. The presented approach could be integrated as a good practice in any other city worldwide. It supports the identification of suitable outdoor tourist places that are memorable, cosy, multifunctional and can be recommended by city guides (mobile or printed books). Every city has many hidden gems that tourists have yet to discover.

The purpose of this article is to explore preference for privacy among people with different demographic and cultural backgrounds. In particular the study aims to investigate the effect of age, gender, previous experience of space and cultural background on people's chosen spatial location for privacy in multi‐bed wards.

A group of 79 subjects were asked to complete a questionnaire on privacy and to select preferred and disliked locations on plans of hospital wards. Spatial data were provided by space syntax analysis (VGA). Possible subgroups in the data were investigated by tests of difference and latent class analysis applied to those spatial attributes which appeared to be relevant to people's preferences on locations for privacy.

The results show that privacy regulation encompasses universal and specific aspects across cultures, age, gender and previous experience of space. Specifically, the results suggest a universal preference for spatial location of privacy across culture, age and gender and a specific significant difference for spatial location of privacy as a result of previous spatial experience. In addition, the VGA integration measure was found to be a highly significant discriminator between preferred and disliked locations for privacy.

There are two particular limitations requiring further study. First, the study investigated only one facet of privacy, i.e. spatial location. More investigation is required to explore the inter‐relationships between spatial location and other facets of privacy, primarily that of intervisibility. Second, only two broader cultures (European and Arabic) were considered.

Ideally it would have been of benefit if a greater number of the people sampled had had direct experience of hospital wards.

At a general level the study supports the notion that there are universal and specific aspects to privacy. At a specific level the research links physical aspects of spatial location (i.e. visibility graph analysis measures) into this discussion.

We formulate generalized maximum entropy estimators for the general linear model and the censored regression model when there is first order spatial autoregression in the dependent variable. Monte Carlo experiments are provided to compare the performance of spatial entropy estimators relative to classical estimators. Finally, the estimators are applied to an illustrative model allocating agricultural disaster payments.

The purpose of this paper is twofold. First to identify economic activities and broader spatial logistics functions that characterise an urban setting, and second to delineate significant spatial logistics employment clusters to represent the underlying regional geography of the logistics landscape.

Using the four-digit Australian and New Zealand Standard Industrial Classification, industries “explicitly” related to logistics were identified and aggregated with respect to employment. A principal component analysis was conducted to capture the functional interdependence of inter-related industries and measures of spatial autocorrelation were also applied to identify spatial logistics employment clusters.

The results show that the logistics sector accounts for 3.57 per cent of total employment and that road freight, postal services, and air and space transport are major employers of logistics managers. The research shows significant spatial clustering of logistics employment in the western and southern corridors of Melbourne, associated spatially with manufacturing, service industry and retail hubs in those areas.

This research offers empirically informed insights into the composition of spatial logistics employment clusters to regions that lack a means of production that would otherwise support the economy. Inability to measure the size of the logistics sector due to overlaps with other sectors such as manufacturing is a limitation of the data used.

The research offers policymakers and practitioners an empirically founded basis on which decisions about future infrastructure investment can be evaluated to support cluster development and achieve economies of agglomeration.

The key value of this research is the quantification of spatial logistics employment clusters using spatial autocorrelation measures to empirically identify and spatially contextualize logistics hubs.

For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.

This paper investigates the impact of governance on economic growth, considering the spatial dependence between countries.

The study employs spatial regression models to estimate the impact of governance on economic growth in a sample of 116 countries worldwide in 2017.

The findings imply that the influence of governance on economic growth is statistically significant. Moreover, if all other economic control variables are constant, 1% increase in governance raises the economic growth on average by 1% at 10%, 5% and 1% significance levels, respectively. Furthermore, each country's rise in economic growth favorably and substantially influences the economic growth of its bordering nations. The unobserved characteristics or similar unobserved environments in adjacent countries also affect its economic growth.

This study adds to the discussion and investigation of the influence of governance on economic growth by considering the spatial dependence between countries, which is lacking in the literature.

This study aims to analyze the relevance of the city spatial structure for smart city innovation from the perspective of agglomeration externalities, and discusses whether there is heterogeneity in innovation across different geographical areas and population scales of cities.

The authors construct the centralization and concentration indexes to conceptualize the city spatial structure of 286 cities (prefecture-level) in China based on the LandScan Global Population Dataset from 2001 to 2016. A fixed-effects panel data model is employed to analyze the relationship between the spatial structure and the innovation ability of smart cities; the results were validated through robustness tests and heterogeneity analyses.

The study found that the more concentrated and more evenly the distribution of urban population, namely the more city spatial structure tends to be weak-monocentricity, the higher the level of innovation in smart cities. The relevance of the weak-monocentricity structure and smart city innovation varies significantly depending on their geographical location and the size of the city. This result is more applicable to cities in the eastern and central regions, as well as to cities with smaller populations.

The adjustment and optimization of the city spatial structure is important for enhancing smart city construction. Unlike previous studies, which mostly use a single dimension of “the proportion of population in sub-centres to the population of all central areas” to measure city spatial structure, the authors employed the spatial centralization and spatial concentration. It is hoped that this study can guide smart city construction from the perspective of the development model of city spatial structure.

Property valuers in England and Wales face a difficult task; they must collect and assimilate data from a variety of sources which differ widely with regard to quality, currency and coverage. This is because of legislative restrictions on access to public sector data sources and organizational constraints on access to property data within the private sector, such as confidentiality and commercial secrecy. Proposes a system for the dissemination of property data for valuation and other property‐related procedures, namely, a National Land Information Service (NLIS). Suggests that a NLIS should be developed using geographical information system technology. Describes a methodology which shows how a spatial analysis technique available on a GIS was used to examine the influence of accessibility on property value.