Transportation Management in Urban Functional Areas

Prelims

The application of last-generation spatial data modelling, integrating Earth Observation, population, economic and other spatially explicit data, enables insights into the sustainability of the global urbanisation processes with unprecedented detail, consistency, and international comparability. In this study, the land use efficiency indicator, as developed in the Sustainable Development Goals, is assessed globally for the first time at the level of Functional Urban Areas (FUAs). Each FUA includes the city and its commuting zone as inferred from statistical modelling of available spatial data. FUAs represent the economic area of influence of each urban centre. Hence, the analysis of land consumption within their boundary has significance in the fields of spatial planning and policy analyses as well as many other research areas. We utilize the boundaries of more than 9,000 FUAs to estimate the land use efficiency between 1990 and 2015, by using population and built-up area data extracted from the Global Human Settlement Layer. This analysis shows how, in the observed period, FUAs in low-income countries of the Global South evolved with rates of population growth surpassing the ones of land consumption. However, in almost all regions of the globe, more than half of the FUAs improved their land use efficiency in recent years (2000–2015) with respect to the previous decade (1990–2000). Our study concludes that the spatial expansion of urban areas within FUA boundaries is reducing compactness of settlements, and that settlements located within FUAs do not display higher land use efficiency than those outside FUAs.

Understanding mobility patterns within Functional Urban Areas (FUAs) is a crucial step toward developing coherent and integrated transport policies. Identifying both differences and similarities in transport behaviors among residents with diverse mobility preferences across FUAs of varying sizes is especially important in light of the economic, administrative, and environmental disparities between core cities and their surrounding municipalities. The primary aim of this study was to examine the influence of risk-related and innovation-related spatial factors on transport choices, depending on the size of FUAs, using data collected through a geo-survey. Statistical analysis led to the creation of a ranked list of factors affecting travel behavior, taking into account preferred modes of transport (walking, single-track vehicles, cars, public transport) and FUA size categories (large, medium, small). The findings help to fill a research gap concerning travel behavior in the three lower OECD-defined categories of FUAs, while also providing a comprehensive overview of 45 spatial determinants (20 related to risks and 25 to innovations).

Urban Atlas 2012 is a powerful geographical dataset that describes land use/land cover at high spatial resolution for nearly 700 European Functional Urban Areas of more than 50,000 inhabitants in 31 European countries (EU28 + EFTA). The objective of the work described in this report was to enrich the Urban Atlas dataset by including estimates of residential population at the vector polygon level.\nThe estimation was done by downscaling, or disaggregating, census population reported at country-specific geometries (‘source geometry’) to the Urban Atlas land use/land cover polygons (‘target geometry’). The downscaling method can be described as a ‘smart’ areal interpolation which combined land use/land cover information from Urban Atlas, building densities from the European Settlement Map and census data. For numerous Functional Urban Areas for which population data were previously only available at coarse resolution (e.g. municipality, or 1 Km2 grid cells), these newly released estimates represent a significant increase in spatial resolution, enabling diverse fine scale analyses for the whole Urban Atlas dataset.\nWith the free-of-charge distribution of this dataset, the number of potential users and applications of the Urban Atlas can further increase. Detailed maps of population density are very important for the study and characterisation of urban areas, and are essential inputs for urban and infrastructure planning and management, disaster risk assessment and mitigation, social policies, and analysis of quality of life and well-being. Moreover, this work further expands the knowledge base of JRC’s LUISA territorial modelling platform, used to assess regional and local impacts of European trends, policies and investments.

Since the 1980's, urban functions being strengthened and urban functional areas expanding with high speed has become the marked character of urbanization in China as urban economic system reformation has being deepened more continuously. Based on the analysis of two-time TM images in 1984 and 2000 in PCI, the author defines the confines of urban functional area, analyzes the growth characters of urban functional area in different size and district in China from 1984 to 2000. The results are as follows: from 1984 to 2000, the total urban functional area increases by 7.04 times. The expanded ranges of the king-sized cities (urban nonagriculture population between 1,000,000 to 2,000,000) and the small cities (urban nonagriculture population less than 200,000) are the biggest ones and their urban functional area increases by 9.6 times and 11.1 times respectively, the latter mostly results from the increase on city quantities, and the super-sized cities (urban nonagriculture population more than 2,000,000) expand the least with only an increase of 2.6 times. At the same time, the index Cf (the elasticity coefficient of urban functional area growth, namely, the ratio of area growth rate and urban functional population growth rate) is built to analyze the growth character of urban functional area, and it is evident that Cf of the big cities (urban nonagriculture population between 500,000 and 1,000,000) and the king-sized cities are the biggest ones, which are 7.36 and 5.55 respectively, and the middle-sized (urban nonagriculture population between 200,000 and 500,000) and small cities' are the least ones, only 3.74 and 3.52 respectively. From 1984 to 2000, urban functional area in South-China district expands the largest with an increase of 12.37 times, and Qinghai-Tibet Plateau district's is the least one with only an increase of 1.14 times. The amplitudes of other districts are between 6 times to 7 times. So the spatial distribution of urban functional area appears the dynamic pattern with the character that the south proportion is up and the north is down and the difference among districts being shortened apparently. During the period, in Northwest Drought district and Southwest district, urban functional area expands most extensively, Cf of which are 9.37 and 6.56 respectively, while in Qinghai-Tibet Plateau district and Southwest district is most intensively, of which are 1.76 and 2.48 respectively. And in other districts, such as North-China, Loess Plateau, the middle and lower reaches of Yangtse River and South-China, their Cf are almost same to the whole country with and average of 4.48.

Purpose Municipal authorities utilize branding as a strategic tool to stimulate local economic growth, attract skills, and increase visitor numbers. This paper seeks to explore the categories of brand attributes and brand values included in brand propositions of Functional Urban Areas (FUA) in Poland. The research adopted a social media-based city branding perspective, focusing on content generated by cities forming the core of FUA’s. These brand propositions were specifically examined through their portrayal on the Instagram platform. Methodology Utilizing a qualitative approach, content analysis was performed on 559 posts from official Instagram accounts of nine core cities of Metropolitan FUAs throughout the height of the tourist season. Human coding method was implemented. Findings The analysis identified “nature,” “cityscape,” and “things to do” as the most frequent brand attributes in the examined FUAs. Among brand values, “excitement,” “sincerity,” and “neatness” were most common, with two additional FUA-specific values: “knowledge” and “playfulness”. Practical implications Metropolitan FUA branding on Instagram appears unplanned and somewhat random. Conclusions from the research might be useful in developing the branding strategy of promotion of the FUAs by highlighting the need for intentional, differentiated brand propositions that leverage unique characteristics and are effectively communicated through social media. Originality This paper contributes to the theory of city branding in two areas: the development of a comprehensive list of potential brand attributes and brand values utilized in urban branding activities, and the empirical verification of the adaptability of this list for analyzing branding initiatives undertaken by cities serving as cores of FUAs.

Urban functional area building carbon emission reduction driven by three-dimensional compact urban forms’ optimization

Motives: Urban mobility models are constantly changing due to economic, social, infrastructural, and geographical factors. Preferences for transport modes vary by country, region, and city, and transport infrastructure and policy have a significant impact on residents’ choices. Additionally, economic factors such as economic crises, public policy decisions, and events such as armed conflicts have a significant impact on the dynamics of mobility in functional urban areas (FUAs).Aim: The main goal of the research was to create groups of countries (clusters) that have similar priorities regarding travel behaviour threats revealed as a result of the economic crisis resulting from the conflict in Ukraine.Results: The study shows that the main threats during the economic crisis (resulting from the war in Ukraine) are economic factors (e.g., ticket prices, an increase in fuel costs), legal factors (e.g., loss of a driving license or passenger transport license), and infrastructural factors (e.g., poor condition of infrastructure). The analysed countries were grouped based on the hierarchical prioritization of threats, using Ward’s hierarchical clustering method. Interestingly, the distance from the conflict epicentre did not significantly influence the prioritization of threat factors influencing transportation behaviour in FUAs. Instead, factors such as membership in international organizations, energy dependence on Russia, the level of economic growth, and transport infrastructure played a more key role in the priority-setting process.

The preservation of spatial order is one of the key objectives of spatial planning. Due to the dynamic changes associated with rapid urbanization, special attention should be paid to cities and the surrounding areas. The aim of this study was to measure spatial order in social, economic and environmental dimensions in the Functional Urban Area of Olsztyn, the capital city of the Polish voivodeship of Warmia and Mazury, and to propose indicators for measuring spatial order. Socioeconomic development should be balanced with environmental protection to promote spatial order and improve the quality of life. Functional urban areas should be regularly monitored to ensure that planning measures contribute to the preservation of spatial order, and to minimize local problems and conflicts. The municipalities belonging to functional urban areas should be regarded not only as distinct territorial units, but also as members of a cohesive area whose development in every dimension of spatial order contributes to an improvement in the quality of life for local communities.

The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management.

Urban functional area recognition refers to refining the main functions of building coverage areas. At present, multisource data analysis is prone to data imbalance, and types with large data volume are more likely to affect data analysis results. Therefore, this study took the main urban area of Taiyuan as the research object and used the Synthetic Minority Oversampling Technology (SMOTE) method to reduce the impact of data imbalance. In this study, the SOMTE method was used to incrementally process the microblog check-in data in the main urban area of Taiyuan, which reduced the phenomenon of data imbalance and further improved the recognition accuracy. The Point of Interest (POI) data were clustered through K-nearest neighbor, and microblog check-in data were semantically analyzed by Linear Discriminant Analysis (LDA). Then, the eigenvalues of the two kinds of data results were obtained by frequency density analysis. Finally, feature fusion was carried out by means of weighted average. The fused data were divided into single and mixed functional areas according to the difference of frequency density, which was rendered and displayed on the ArcGIS platform, so as to realize the visual identification and division of urban functional areas, and the results were compared with Gaode Map. The experimental results showed that this method can effectively identify urban functional areas with a recognition accuracy of 85%, which provided reference value for the planning and research of urban functional areas in the future.

Heavy metal pollution in urban river sediment of different urban functional areas and its influence on microbial community structure.

Academic discussions have frequently examined the interrelation between regional employment growth and firm locations. Two growth patterns emerge: employment growth induced through new firm locations or vice versa, where firms locate in areas experiencing employment supply growth. The specific causal relationship responsible for regional employment growth in Germany remains uncertain. In the German context, however, more research is needed to identify contributors to employment growth, as most existing studies rely on highly aggregated data or focus on specific case studies. This paper aims to approach this subject by using a uniquely matched dataset of firm locations and the individual employment of 480 multi-locational firms in the knowledge economy and comparing it to total employment in Germany. We assume that a change in knowledge-intensive firms’ employment may affect regional employment growth. The study uses longitudinal historical employment data at the functional urban area (FUA) level from 1999 to 2019, aggregated to knowledge-intensive high-tech and advanced producer services (APS) sectors. The analysis employs aggregated and individual Granger causality tests, evaluating the relationship between employment in knowledge-intensive sectors and overall employment change. Results are spatialised using GIS to provide evidence of where the Granger causalities occur at the FUA level in Germany. Findings indicate that, in general, knowledge-intensive employment growth Granger causes total employment growth in a few economically more active FUAs. In contrast, for a greater number of FUAs, total employment Granger causes knowledge-intensive employment.

Functional Urban Areas (FUAs) leads to a better knowledge of urban spatial organisation, which may play a significant role in regional policy making and may be helpful in understanding the connection between urbanisation and demographic development. An explanation of population change in urban regions can be associated the second demographic transition comprising fertility decline below replacement level and postponement of births. The aim of this paper is to focus on establishing similarity patterns and anomalous values of selected demographic variables in the cores and peripheral areas of Functional Urban Areas. At the background of this study lies an assumption that population development of FUA's is shaped by different factors connected with second demographic transition and migrations. To achieve the aims the following demographic characteristics were used: population growth rate, dependency ratio, rate of natural increase, the net migration rate, and the dynamic economic ageing index, Spatial methods play an increasingly important role in contemporary socio-demographic research. In order to identify spatial systems Global Moran Statistics and the Local Indicators of Spatial Association (LISA) including Local Moran statistics as well as Getis-Ord Gi* statistics were used. The research showed global and local autocorrelation of demographic processes in Functional Urban Areas in Poland, namely population growth, natural increase, net migration and population ageing. The use of local Moran's I statistic and the Getis-Ord Gi* method has led to identification of spatial clusters and dispersions representing different demographic variables. Spatial autocorrelation methods can be useful in an analysis of demographic variables including changes in time. The main contribution of this study to the research on demographic processes in urban areas was an application of spatial groupings techniques not only to find out similarity and dissimilarity patterns of demographic indicators but also to apply this findings for the needs of spatial planning.

The accurate identification of urban functional areas is of great significance for optimizing urban spatial structure, rationally allocating spatial elements, and promoting the sustainable development of the city. This paper proposes a method to precisely identify urban functional areas by coupling Open Street Map (OSM) and Point of Interest (POI) data. It takes the central urban area of Hangzhou as a case study to analyze the spatial distribution characteristics of the functional areas. The results show that: (1) The central urban areas of Hangzhou are divided into 21 functional areas (6 single functional areas, 14 mixed functional areas and 1 comprehensive functional area). (2) The single functional areas and the mixed functional areas show the geographical distribution characteristics of the looping stratification, which means “Core-periphery” differentiation is obvious, and the comprehensive functional area is relatively scattered. (3) The mixed degree of regional function with ecological function and production function is low while comprehensive functional areas are usually associated with higher potential and vitality. (4) The identification results are in great agreement with the actual situation of Hangzhou central urban area, and the method is feasible. Therefore, this paper can provide a reference for urban development planning and management.