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This study aims to examine the evolution of spatial–temporal patterns in China’s hotel industry from 1978 to 2018.

A database comprising over 140,000 hotels with more than 30 rooms was created. The exploratory spatial–temporal data analysis (ESTDA) method, based on space–time cube model, was used to explore and visualize the spatial–temporal pattern of hotels.

The Chinese hotel industry can be divided into two development stages, namely, a large hotel-dominant stage before 2000 and a small–medium-sized hotel-dominant stage after 2000. China’s prefecture-level cities were clustered into four tiers. The higher the tier, the earlier the city will initiate hotel development. The Chinese hotel industry has four continuous hotspots (the Yangtze River Delta, Pearl River Delta, Bohai Rim and Sichuan and Chongqing) and some temporary hotspots.

This study lacks quantitative investigation, which could show the underlying mechanism of the evolution of the Chinese hotel industry.

This study is the first to investigate China’s hotel evolution over 40 years by applying big data and the ESTDA method. The systematic and evolutionary exploration will enable hotel researchers to understand the spatial–temporal nature of hotel distribution better. Introducing the ESTDA method into tourism and hotel research also provides an additional tool to researchers. Hotel investors and operators, city and tourism planners and market regulators can learn from the evolution of location patterns to make better where and when decisions.

Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown.

This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance.

Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences.

A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns.

The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs.

This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

Purpose – Evidence suggests that during the 1990s, many US metropolitan areas saw fundamental changes in the spatial distribution of household income. Following two decades of increasing economic segregation, many metropolitan neighborhoods saw declines in economic segregation, particularly those neighborhoods located within central cities and rural areas. This paper adapts the Spatial Ordering Index proposed by Dawkins (2007b) to explore these trends.

Methodology/Approach – Using US Census data, I calculate economic segregation indices for a sample of 205 US metropolitan areas in 1990 and 2000 and decompose changes in the indices into portions attributable to changes in the spatial distribution of households and portions capturing changes in the spatial distribution of aggregate income. I also examine regional variations in the decompositions.

Findings – The results suggest that changes in the spatial distribution of households and of income each influenced metropolitan economic segregation in different ways during the 1990s. Furthermore, the spatial dynamics of income segregation exhibited significant regional heterogeneity.

Originality/Value of paper – This paper presents a new approach to measuring the dynamics of economic segregation.

The purpose of this paper is to develop a systemic approach to evaluate physical condition of water pipeline infrastructure with limited condition assessment data that can help asset managers prioritize capital investments in maintenance projects for urban water pipeline systems.

Spatial pattern analyses are conducted in this research to find the spatial pattern of the service life of pipelines. Based on the spatial relationship, the critical areas where groups of pipelines with short service life are likely to be found were located using spatial statistical analyses. A visualized platform was also developed and used to validate the implementation of the proposed approach with the case study of urban water pipeline infrastructure in a city in the Midwest region of the USA.

The results of the spatial pattern analyses reveal that water pipelines are spatially clustered based on their service life. Further, it was found that on average the pipelines in the center of a city have longer service life while the average expected service life of the pipelines in the marginal areas is shorter. The interpolation method produced raster data with continuous information about the service years of pipelines that are useful for asset maintenance planning.

With the limited data, the proposed approach enables identification of the critical area of water pipelines with the likelihood of shorter service life. This result can be used as a priority rule for a rehabilitation plan and contributes to shifting from a responsive to a preventive approach in underground asset management.

This study aims to explore the pattern of urban residents’ socio-spatial distribution in a small-sized city where the local housing market capacity and variety is limited.

Spatial variation was reflected by two different analysis. First, factor analysis was applied to determine the major dimensions of the social, economic and housing environment in the investigation area. Second, Kriging maps, which depict the socio-spatial distribution pattern of the households according to major dimensions, were produced by interpolating factor scores on a continuous surface. Those were supported by complementary exploratory analysis to deepen the discussion.

Homogenous distribution of similar groups to housing areas and low inner differentiation particularly within lower income neighborhoods are the noticeable results of the analysis set. Ethnicity and income differentiation are the principal determinants of socio-spatial distribution pattern in our case. The constraints of the local housing market are seemed to facilitate spatial separation. Disadvantaged population groups are limited to small niches within the urban fabric; they are relegated to poor quality neighborhoods or to unpopular inner-city housing estates.

This research has been performed for the small size city in Turkey and may not hold for other areas, even though the methodology can be replicated and the mechanisms at play are quite similar elsewhere.

The internal differentiation of urban residents’ is worth investigation to develop consistent housing and planning policies to overcome prospective social exclusion problems. This study has a potential of remarking the importance of policy-based economic and housing development in smaller cities in Turkey.

Analyses displayed a sectoral structure of the distribution of urban residents but lower inner differentiation within neighborhoods. Limitations of the housing stock facilitate substantial level of isolation to the extent of ethnicity. Two different ethnic groups are confined to small niches, and they are ethnically and economically tied down to their neighborhoods. The physical properties and the quality of both dwellings and the housing environment are the poorest in these areas, and these are unpopular housing areas by the majority of the population. These findings are supposed to give direction of setting consistent housing policies in the case area.

This research is one of the initial research on socio-spatial distribution of urban residents to housing areas in Turkey. It is also one of the rare examples of socio-spatial differentiation study in small-sized city in the literature. The authors have shown that socio-spatial differentiation would be severe even in small size housing markets as opposed to expectation.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

In this paper we consider the complementary questions: in what sense do local dynamics prescribe global spatial patterns and to what extent does a global pattern impose constraints on local interactions. From the standpoint of results from mathematical system theory, it is argued that a modeling approach starting from observed patterns and passing to local dynamics is vastly to be preferred to proceeding in the opposite direction, the usual approach mimicking the procedure followed in the physical sciences. The paper concludes with a discussion of the role of anticipatory decision making and adaptation in the stabilization of certain properties of dynamical spatial processes.

The purpose of this paper is to investigate area-level labour market dynamics from a spatial perspective. This analysis is aimed at better understanding what socio-economic actors are associated with shifts in unemployment rates across a major metropolitan city.

Based on two waves of New Zealand census data, this paper combines a seemingly unrelated regression approach (allowing for relaxation of the assumption that residuals from models of different employment states are unrelated) with a spatial lag model.

The key socio-economic drivers associated with intra-city employment dynamics were vehicle access, dependency rates and educational attainment. Importantly, the identification of spatial autocorrelation with respect to employment status patterns within this major New Zealand city motivates a case for heterogeneous employment policies across the city.

This research improves the understanding of changes in labour market status rates within a city region. This is done by inclusion of two important considerations: a spatial perspective to labour market dynamics at an intra-city level; and formally modelling the interdependence across the four potential labour market outcomes (being full-time, part-time, unemployed or out of the labour force). Overall, there was clear empirical support for the need to include spatial considerations when using targeted policy to help lift areas out of unemployment.