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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 research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

One of the characteristics of our age is urbanization and the increase in the population of cities and the development of small and large cities. In fact, the sustainable development of a city, which in its general sense means all-round growth and expansion without contradiction, with two main goals: (1) improving the quality of life in terms of the capacity of the environment and (2) responding to the needs of the present generation without restrictions for the future ones. But according to the fifth wave theory, entering the first edge of the future from 2020 to 2030, with the development of urbanization, we are witnessing instability in recent decades. Depletion of forests, thinning of the ozone layer, accumulation of greenhouse gases, noise pollution, threatening air pollution in cities are among these indicators in urban areas. Other important indicators also show the social and economic instability of the world community. The increase in poverty, economic growth without job creation, increase the gap between developed and developing countries, social disintegration, and the sense of citizenship of the residents of local communities and the class gap are among these indicators. Therefore, since 1970, with the advancement of technologies, today the realization of urban development depends on attention to the factors of innovation, digitalization, and intelligentization. In the concept of development, aspects, domains, and scales become important. This improvement of the current situation will be realized when it brings all-round intelligence without conflict and contradiction in meeting the tangible and intangible needs of this society. Growth in material and spiritual aspects, from local to global scales and in different economic, cultural, educational, social, technological, and even political fields. Therefore, paying attention to the quality makes the concept of development more effective. These qualities are aligned with the concept of sustainability. In this chapter, U-city, smart sustainable mobility through 5th Wave, i-Sustainability Plus, and doost cultural theory (DCT) will be probed as a new concept of urban living for blue-green smart city and mobility. In addition, challenges made some countries not to prefer to utilize smartness and ubiquitous idea as a tool for their problems, will be explored.

This chapter aims to provide a comprehensive understanding of the urban outcomes of smart city projects, focusing on their primary objectives. The first objective is to facilitate the management and flow of information, data, and resources to enhance resource efficiency, sustainability, and the quality of life for citizens and stakeholders. This chapter offers insights into the urban objectives of smart city projects within the local ecosystem, with a specific emphasis on digital and key urban outcomes. It provides an overview of the digital outcomes, including the advancement of digital systems for safety and urban monitoring, the provision of customized digital services, and the promotion of citizen engagement through digital platforms. This chapter also evaluates the environmental outcomes of smart city projects, such as improved quality of life, increased urban efficiency, and contributions to a sustainable environment. To provide a well-rounded understanding, interviews with policymakers and city managers, as well as case studies from cities like London, Medellin, Helsinki, Singapore, Girona, and San Diego, are incorporated. Furthermore, this chapter incorporates data and findings from top-tier international journals to provide a clear understanding of the impact of smart cities on the local ecosystem.

The “unplannable” is a welcomed exception to the formal order of urban planning. This opinion article explores some examples of informal urbanism and discusses its ambiguous relationship to public space and unplanned activities in the city. The informal sector offers important lessons about the adaptive use of space and its social role. The article examines the ways specific groups appropriate informal spaces and how this can add to a city’s entrepreneurship and success. The characteristics of informal, interstitial spaces within the contemporary city, and the numerous creative ways in which these temporarily used spaces are appropriated, challenge the prevalent critical discourse about our understanding of authorised public space, formal place-making and social order within the city in relation to these informal spaces.

The text discusses various cases from Chile, the US and China that illustrate the dilemma of the relationship between informality and public/private space today. One could say that informality is a deregulated self-help system that redefines relationships with the formal. Temporary or permanent spatial appropriation has behavioural, economic and cultural dimensions, and forms of the informalare not always immediately obvious: they are not mentioned in building codes and can often be subversive or unexpected, emerging in the grey area between legal and illegal activities.

Existing performance assessment frameworks, such as the Sustainable Development Goals (SDGs), struggle to incorporate diverse voices and representations of heterogeneous contexts. Cities, in particular, present a challenging context for sustainability performance assessment as they pursue new forms of governance based on the multiplicity of actors and inter-organisational collaboration. This study explores how sustainability performance accounts are created at the urban level within collaborative forms of governance and amidst the plethora of available devices for performance assessment.

This study adopts a case study approach. Qualitative methods are mobilised to study a large European project focused on the urban transition to a circular economy in six participating cities. The construction of sustainability performance accounts is studied via the Actor-Network Theory lens.

The study highlights that when it comes to sustainability assessment in city initiatives, existing performance assessment devices are adapted and modified to fit local needs and other sources of performance information are spontaneously mobilised to address the different dimensions of sustainability.

This study contributes to the public sector accounting literature by explaining the process of modifying existing devices for performance assessment to allow for the co-creation of accounts and by illuminating the spontaneous way in which performance information is generated and combined.

This chapter examines the six smart city dimensions that serve as pillars in smart city projects. These dimensions are crucial in the development and evaluation of smart city initiatives, representing key areas for consideration. This chapter offers a detailed analysis of the smart city ecosystem, focusing on the governance, environment, people, living, mobility, and economy dimensions. It challenges the prevailing media portrayal of the smart city strategy and engages in the current academic debate surrounding these dimensions. This chapter defines, discusses, and explains each dimension, incorporating case studies from cities such as Copenhagen, San Francisco, Lisbon, and Barcelona. It also includes interviews and factual data to highlight the internal implementation and objectives of the smart city within each dimension. This chapter provides a comprehensive understanding of the smart city ecosystem, its implementation, and the potential benefits and challenges associated with each dimension.

This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

Urban digital transformation has become a key strategy in global countries. This study aims to provide a comprehensive and dynamic exploration of the intrinsic traits associated with urban digital transformation, in order to yield detailed insights that can contribute to the formulation of well-informed decisions and strategies in the field of urban development initiatives.

Through analysis of parallels between urban digital transformation and gyroscope motion in physics, the study developed the urban digital transformation gyroscope model (UDTGM), which comprises of seven core elements. With the balanced panel dataset from 268 cities at and above the prefecture level in China, we validate the dynamic mechanism of this model.

The findings of this study underscore that the collaboration among infrastructure development, knowledge-driven forces and economic operations markedly bolsters the urban digital transformation gyroscope’s efficacy.

This research introduces a groundbreaking framework for comprehending urban digital transformation, potentially facilitating its balanced and systemic practical implementation.

This study pioneers the UDTGM theoretically and verifies the dynamic mechanism of this model with real data.