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Despite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.

The model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).

Evaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.

Presents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.

Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.

The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.

The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.

COVID-19大流行对全球旅游业产生了重大影响，国际旅行受到了限制的影响最为严重。国内旅游也面临着重大挑战。本文分析了COVID-19大流行对西班牙国内旅游的影响，重点关注从马德里（该国首都）到其他旅游目的地的旅行。

本研究使用移动电话数据研究了2019年、2020年和2021年夏季旅游出行的演变。采用回归模型解释了各目的地游客数量。

大流行不仅导致了旅游流量急剧下降，还扰乱了国内流动系统的总体模式。获胜的目的地通常是马德里附近的地区和人口较稀少的目的地，而城市目的地是主要的输家。国内游客的偏好在收入群体之间有明显差异，但旅行量的减少只显示出边际差异。

本文展示了使用移动电话数据分析研究外部冲击（如COVID-19大流行）对旅游目的地的不均匀影响的潜力。该方法考虑了区域或省份内的空间弹性异质性。通过整合收入信息，该分析为高度详细的空间数据引入了社会维度，超越了传统在区域或国家水平进行的研究。

Adaptive reuse entails the physical modification of abandoned architectural structures, with the activation of processes and practices leading to the re-incorporation of heritage into the contemporary life of communities. This transformation entails an affective adaptation, a re-modulation of how citizens attune to a built environment that has been returned to urban, shared forms of use. By observing the emotional ties that are established between subjects and the spaces they inhabit, affecting forms of dwelling, attachments and corporeal responses, the author can clarify how adaptation purports this affective modification, where the original ambiance is not necessarily altogether overwritten, but may rather merge with the supervening situation to give life to unique assemblages of spatialized feelings.

Drawing from contemporary phenomenological theories, with their specific focus on the affective and embodied dimension of lived experience, this paper describes and discusses two instances of adaptive reuse, one in Brussels, the second in Rome, highlighting their different processes and spatial outcomes.

The paper implements recent literature on spatial experience to bring to light conditions found in cases of adaptive reuse. By describing the generators of shared emotions – objects, movements, expressions, materialities, textures – it highlights how the layering of the physical world can lead to both the domestication of affects and to discrepancies and discontinuities in the fabric of experienced space.

There is only a limited literature dedicated to the description of adaptive reuse processes from the contemporary phenomenological perspective. This kind of description can clarify the dynamics unfolding between citizens and experienced space in cases of heritage reuse.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.

In this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.

The proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.

It can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.

It can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.

In this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

This paper aims to explore the ambiguity and limitations of measuring firm-level multinationality (FLM) using theoretical and empirical comparisons of existing methods. The paper puts forward a list of five key aspects that collectively serve as a tool for researchers to select the most appropriate method for future research and as a basis for the future development of methods.

Firstly, the author reviews existing methods of measuring FLM and consolidates findings into five key aspects. Secondly, the author uses the aspects to compare existing methods theoretically, and subsequently, the author groups them into three distinct streams. Thirdly, the author compares existing methods across a sample of the 35 largest European MNEs by sales in 2020 to identify and demonstrate the ambiguity and limitations of these methods.

The author identifies the five key aspects of measuring FLM: framework, aggregation, segmentation, metrics and indicators. Using empirical comparison, the author empirically confirms the limitations highlighted in the literature and shows the differences and inconsistencies among methods, which cause confusion rather than clarity in the extant literature. Additionally, the author emphasises that three distinct streams further drive the debate on the regional/global nature and present further limitations of methods not mentioned in the literature to date.

This paper provides the most comprehensive review of the existing literature on FLM, resulting in five novel aspects of measuring FLM. The analysis of a sample of 35 European firms demonstrates and identifies the ambiguity and limitations of FLM-measuring methods.

The purpose of this study is to examine the influence of urbanization on energy consumption, including economic growth, globalization and “foreign direct investment (FDI)” inflow as control variables.

This study uses yearly panel data from 19071 to 2018 on five selected South Asian economies. It applies the “pooled mean group (PMG)” estimator and the “Dumitrescu-Hurlin (D-H)” panel causality test.

The PMG estimators reveal that urbanization causes energy consumption negatively in the long run because of an unusual and messy urbanization process. At the same time, it has no impact on the latter in the short run. Per capita income has both long- and short-run positive influences on energy use. Globalization causes energy consumption positively in the long run but does not affect it in the short run. FDI inflow has a strong positive impact on energy use in the long run and adverse effects in the short run. The Dumitrescu–Hurlin causality test reveals feedback relationships between “urbanization and energy consumption,” “globalization and energy consumption” and one-way causation from “per capita income to energy consumption.” It validates the findings of the PMG estimators.

The results of this study indicate that South Asia may focus on enhancing the availability of energy in the region and producing more renewable energy to add to its energy portfolio to meet growing energy demand, particularly among urban dwellers. Moreover, they should raise their real per capita incomes and augment the standard of living of low-income city dwellers to make urbanization more serviceable and comfortable.

This study is original. As far as the author is aware, this is a maiden attempt to investigate urbanization's effects on energy usage in South Asia in the preview of globalization and FDI.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

The vast amounts of waste generated today threaten economies and societies due to high environmental and management costs. The aim is to investigate the short- and long-term patterns of municipal waste generation (MWG) in response to socio-economic and demographic growth variables at national and regional levels.

A panel data approach employing ordinary least squares (OLS), fixed effects (FE), random effects (RE), fully modified least squares (FMOLS) and error correction model (ECM) techniques. A sample of 28 European countries (2000–2020) and 44 European Union (EU) regions (2000–2018) were selected.

During periods of economic growth and higher employment rates, consumer confidence tends to increase, leading to elevated levels of consumer spending and consumption. Intensification in the production factors, specifically capital and employment, results in an upsurge in MWG, thereby creating a cycle where waste generation becomes deeply entrenched in the economic system in both the short and long terms. Rapid population growth, attributed to higher fertility rates, is associated with increased MWG. At the regional level, a double-aging process and a shift toward an aging population exert less pressure on MWG in both the short and long term. Promoting higher levels of environment-oriented human development yields various benefits, including the generation of greater knowledge spillovers, enhanced environmental literacy, a shift toward circular thinking and the promotion of greener entrepreneurship. Increased R&D expenditures facilitate the development of innovative waste reduction technologies, fostering improvements in waste management techniques, recycling processes and the utilization of sustainable materials.

The research examines the short- and long-term adjustments of MWG in response to changes in macroeconomic variables from low aggregation (countries) to high aggregation (regions). By analyzing the relationship between economic growth, urbanization, healthcare system quality, labor market functioning, demographic trends, educational level, technological advancement and MWG, the study fills a research gap and enhances understanding of waste management interventions. However, data availability and waste statistics accuracy should be considered. Future research could explore the relationship between macroeconomic variables and waste generation in sectors beyond MWG, such as industrial or construction waste, for a more comprehensive understanding of waste generation as a whole.

The positive correlation between economic activity levels and waste generation in both the short and long terms, emphasizes the criticality of investing in waste reduction and recycling infrastructure to mitigate landfill waste. The negative correlation between population density and waste generation stresses the importance of strategic waste facility placement in low-density areas. To effectively manage higher MWG, tailored waste collection systems and initiatives promoting healthy lifestyles are of immense importance. The positive relationship between employment rates and waste generation underscores the necessity of waste reduction programs that generate employment opportunities. The positive correlation between fertility rates and waste generation emphasizes the need for the expansion of extended producer responsibility programs to include products and materials specifically associated with families and child-rearing. Education campaigns and governmental support for research and development (R&D) in waste reduction technologies are also integral components of an effective waste management strategy.

The short- and long-term adjustments of MWG reacts to shifts in macroeconomic variables from low aggregation (countries) to high aggregation (regions). Previous research has neglected the long-term information contained in variables by not incorporating the lagged error correction term (ETM). Neglecting this aspect could result in imprecise estimates of the elasticities.