Analyzing the literature covering public management, the author noticed that the urban planning is a crucial fac-tor in urban development. Cities which have an adequate intellectual resources and proper institutions as well as developed infrastructure are called smart cities. According to the author, proper local spatial development plans should be applied in those cities. Crucial parts of the city, places of the highest investors interest such as technology parks, R&D companies, business incubators, technology transfer centers and industrial complexes should definitely be incorporated in these plans. The ISO 37120 Standard is the most practical method to measure a city’s perfor-mance. The factor which decides about special management is the level of investment pressure. If this indicator is decreasing then the area does not have to be covered by local spatial development plans.
This elaboration aims to examine the role of the smart city in urban management. The research shows the relationships between coverage planning, investment pressure and green areas. The main result is the author’s classification of selected 34 Medium-Size Cities in Poland. The test procedure exploited taxonomic methods as Ward’s hierarchical analysis.
This paper presents the use of multi-criteria decision-making (MCDM) for the evaluation of smart cities. During the development of the method, the importance of the decision-making approach in the linear ordering of cities was presented. The method of using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was proposed for the preparation of ranking. The method was verified by the application in the measurement of energy performance in smart cities. The authors conducted a literature review of research papers related to urban energy and MCDM published in the period from 2010 to 2020. The paper uses data from the World Council on City Data (WCCD). The research conducted allowed for the identification of the most popular MCDM techniques in the field of urban energy such as TOPSIS, AHP and DEA. The TOPSIS technique was used to organize and group the analyzed cities. Porto took the top position, whereas Buenos Aries was the last.
The effects of urban transport are highly concerning. The rapid urbanization and motorization in smart cities have a huge impact on sustainability. The goal of the paper is to analyse the smart cities selected, in terms of the urban transport. This paper presents an overview of research works published between 1991 and 2020 concerning urban transport and MCDM (multi-criteria decision making). The author highlights the importance of decision-making criteria and their weight, as well as techniques. Seven criteria and forty-four objects were used as the input of the approach. The entropy weight method was used to compute the weight of each criterion. The TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) was applied to calculate the assessment and ranking of transport performance for each smart city. Portland was found to be the best location for transport enterprises and projects; Tbilisi was ranked last. The values of the relative closeness coefficient ranged from 0.03504 to 0.921402. Finally, some suggestions for future research are discussed.
The smart cities concept plays an important role in urban management worldwide as well as should be implemented with the participation of residents and with consideration for their needs. This study examines an analysis of the smartness diversity of European cities on the basis of the International Organization for Standardization 37120 norm dealing with sustainable development of communities. Cities with a higher level of certification exhibit more effective management and their residents show greater commitment to civic life. The assessment of the level of smartness was based on statistics collected by the World Council on City Data using Multidimensional Comparative Analysis. Hellwig’s (1968) synthetic indicators were used to create a ranking gauging the level of smartness of European cities which were classified into four groups. The investigation shows large disparities in the smartness of selected European cities.
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