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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.

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 paper aims to clarify the spatial connection characteristics and organization mode of logistics economy of 21 cities in Guangdong Province under the background of the integrated development of Guangdong, Hong Kong and Macao Bay area, and explore the spatial development characteristics and influencing factors of logistics economy in Guangdong Province.

This paper constructs the development level model of urban logistics economy in Guangdong Province from three aspects: demand level, supply level and support level, and uses the entropy weight method to measure the development level index of urban logistics economy in Guangdong Province. Then, the traffic accessibility index model is used to measure the traffic accessibility index between cities in Guangdong Province. Finally, using the social network analysis method, combined with the development level index of urban logistics economy in Guangdong Province and the urban traffic access index in Guangdong Province, this paper analyzes the spatial connection characteristics and influencing factors of logistics economy network in Guangdong Province.

There are regional differences in the development level of logistics economy in Guangdong Province; The overall network density of its logistics economic connection is large, but there is an imbalance in the network structure, and the core edge phenomenon is obvious; Logistics economic space presents the characteristics of double core development.

Because the research object is the spatial connection characteristics of logistics economy in Guangdong Province, the research results may lack universality. Therefore, researchers are encouraged to put forward further tests.

By studying the spatial connection mode of logistics economy in 21 cities in Guangdong Province, China, this paper promotes the original methods and empirical contributions, and constructs the research framework of spatial relationship of logistics economy. This research framework is universal to a certain extent.

This paper is conducive to promoting the integrated development of logistics economy in Guangdong Province and improving the balance of regional development of logistics economy.

Firstly, this study provides a new perspective to understand the spatial relationship and spatial spillover of logistics economy from relational data rather than attribute data. Secondly, This study enriched and broadened the research topic of spatial correlation of logistics economy. Thirdly, this research aims to promote the original methods and empirical contributions. Specifically, this study establishes a comprehensive research framework on the spatial network structure of logistics economy.

This study proposes spatial origin-destination threshold Tobit to address spatial interdependence among bilateral trade flows while accounting for zero trade volumes.

This model is designed to capture multiple forms of spatial autocorrelation embedded in “directional” trade flows. The authors apply this improved model to export flows among 32 Asian countries in 1990.

The empirical results indicate the presence of all three types of spatial dependence: exporter-based, importer-based and exporter-to-importer-based. After further considering multifaceted spatial correlation in bilateral trade flows, the authors find that the effect of conventional trade variables changes in a noticeable way.

This finding implies that the standard gravity model may produce biased estimates if it does not take spatial dependence into account.

This paper attempts to offer an improved model of the standard gravity model by taking spatial dependence into account.

This research investigates the effect of accessibility to points of tourist interest (buffer) and direct and indirect spatial spillover effects of agglomeration economies on tourism industry revenues in Spain.

Data were collected from the Bureau van Dijk's (BvD) Orbis global database. The data were analysed using a spatial econometric model and the Cobb–Douglas production function.

This study reveals that hotels located inside the buffer zone of points of tourist interest achieve better economic outcomes than hotels located outside the buffer. Furthermore, the results show that there is a direct and indirect spatial spillover effect in the hotel industry.

The results provide valuable information for identifying areas where the agglomeration of hotels will produce a spillover effect on hotel revenue and the area of influence of location characteristics. This information is relevant for hotels already established in a destination or when seeking a location for a new hotel.

The results of this study can help city planners in influencing the distribution of hotels to fit desired patterns and improve an area's spatial beauty.

The paper provides insights into how investment, structural characteristics, reputation and location affect hotel revenue.

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns.

Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions.

Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others.

Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

This study aims to understand customer’s assessments of neighborhood stores during the COVID-19 pandemic through the influence of in-store environmental factors on patronage intention.

Online survey with 528 participants about the last shopping trip in neighborhood retail. The authors performed data analysis using structural equation modeling techniques.

High-perceived spatial crowding negatively influences shopping experience value perceptions, while human crowding influences patronage intentions through increased perceived hedonic value.

Results suggest that purchase experience at well-known neighborhood stores during a sanitary crisis is becoming less convenience-oriented and a substitute for leisure activities due to social distancing.

The findings elucidate the social function of neighborhood convenience retailing during the COVID-19 pandemic. The results emphasize that a pleasant shopping experience arising from a good relationship with shopkeepers and other customers is more influential on patronage intention than a good product assortment and store layout.

This paper contributes to the survival of small neighborhood businesses during the financial crisis installed due to Covid-19 by helping businesses become more attractive to their consumers and competitive in the new context.

The combined context of the health crisis due to COVID-19 and neighborhood retail of an emerging country raises the need for tests to better understand established marketing theories. Based on this rationale, this work intends to replicate and extend selected previous findings to the new environment dictated by the pandemic.

Drawing on a user-centred design perspective, the purpose of this paper is to (i) provide an overview of three contextual user research methods, namely, spatial walkthroughs, experience curve mapping and card sorting, (ii) exemplify their applications in different case studies and (iii) compare the methods according to their contributions for the study of users’ workplace experiences. Previous workplace studies with qualitative approaches mainly rely on methods such as interviews and observations. Although these methods provide rich data, the understanding of office users, their use situations and finding more fitting workplace designs can benefit from deeper user experience insights.

Three methods and their variants were tested in studies of user experience in flexible offices: spatial walkthroughs, experience curve mapping and card sorting. The methods were tested during workshops and interviews in four case studies with a total of 114 participants.

Spatial walkthroughs were more immersive and provided the most insights on the actual context with respect to spatial design qualities, while experience curve mapping enabled understanding the temporal aspects of the user experience and card sorting enabled exploring user experiences with respect to predetermined spatial qualities and contextual aspects.

Spatial walkthroughs, experience curve mapping and card sorting methods have not previously been applied in workplace studies. They facilitate dialogue, participation and user involvement and provide insights for making evidence-based recommendations for designing or redesigning office environments that fit users’ needs and preferences.