Boundaries Of Agglomerations Research Articles

Оценка количества малых и средних предприятий в границах городов и агломераций: применение методов машинного обучения к данным России

Introduction: the development of agglomerations, which, as research shows, are becoming productivity growth poles, is one of the priorities in the spatial development policy in Russia. Despite numerous discussions, the term “urban agglomeration” is currently absent in federal legislation, but it is widely used in the regulatory framework at other levels of government. This leads to a lack of coordinated, accurate, and up-to-date information on the size and structure of agglomerations, thus bringing forward the need to formulate a well-based approach to identify their economic and geographical boundaries. Objectives: developing an approach to identify the boundaries of agglomerations in Russia, considering the spatial distribution of economic activity. Methods: machine learning, statistical analysis. The use of these methods makes it possible to identify the boundaries of agglomeration with high accuracy using open data on the height of the urban area. Results: the article presents the results of spatial clustering algorithm (DBSCAN) testing on residential development data in Russia; a list of “sustainable municipalities clusters” defining the agglomerations boundaries has been identified and estimates have been made for the number and revenue of firms and SMEs within the largest cities and agglomerations boundaries defined both analytically and formally in regulatory documents. Using the example of individual regions and average data for Russia, it has been shown that the number of firms (total and those registered as SME) within agglomerations can be underestimated by more than one-third and total revenues – by almost 60 %. Conclusions: considering sustainable municipalities clusters can improve the accuracy of agglomeration effects and business activity estimates and contribute to the formation of more accurate government support measures.

Socio-economic situation of Russian urban agglomerations in 2015–2021

The article provides a comparative assessment of the key socio-economic features and trends in the development of urban agglomerations of Russia in 2015–2021. A distinguishing characteristic of the study is the approbation of a method for assessing the level of economic development of agglomerations using data from the Federal Tax Service of Russia on the volume of tax revenues. The analysis confirms that agglomerations provide an extremely high contribution to the economic development of the country and continue to form the basis for the growth of the taxable economy. The most part of contribution to country development indicators is still provided by the largest Moscow metropolitan agglomeration, the concentration of resources within its borders has only intensified in recent years. The differentiation of the other agglomerations also stays great, and the cumulative contribution of them (in sum) by a number of indicators is comparable to one of Moscow agglomeration. The best economic results are shown by agglomerations with industrial specialization, but the development of the economic complex in them does not have a significant stimulating effect on the growth of labor market indicators, household incomes and service sectors. The least developed agglomerations are ones with a high role of «shadow» sectors in the economy and «administratively designated» ones (Ulan-Ude, Penza, Ryazan), located in regions of the country with a depressed economic situation. Reduced values of development are also typical for agglomerations of the Volga region. At the spatial level within individual agglomerations, there is a clear manifestation of center-peripheral differences by most indicators, however, the study showed that the level of development of the cores of agglomerations and their suburbs is not always high, and the positive influence of the agglomeration core (with the exception of Moscow) rarely spreads beyond the nearest belt of municipalities. The results of the study suggest a narrowing of the approach to delimiting the boundaries of agglomerations and prove that the current state measures for the development of the agglomerations (except Moscow one) have not yet made a significant impact on accelerating economic growth and expanding the geography of the influence of agglomeration factors.

Геоинформационный мониторинг демографического развития Краснодарской городской агломерации

Introduction. At the present stage of development in Russia, and in the context of the ongoing transformation of the settlement system in the country, the most important role is played by urban agglomerations, which concentrate significant demographic and economic potential. Krasnodar monocentric urban agglomeration has an advantageous geographical location and is situated in the focus of transport highways connecting the center of the Russian Federation with the ports of the Black and Azov Seas, resorts of the Black Sea coast and the Transcaucasia. It is a major node in the basic framework of population settlement and a significant point on the agglomeration map of Russia, which makes it important to study its demographic development and determine demographic potential. Materials and research methods. The information base of the study is the data of the Federal State Statistics Service of the Russian Federation, the database of indicators of municipalities and other departments for the period from 1989 to 2021, by population, demographic and migration indicators, the territorial structure of the migration flow. The definition of agglomeration boundaries is based on the methodology of the Institute of Geography of the Russian Academy of Sciences and the use of GIS technologies. The medium-term forecast of the population of the agglomeration is made on the basis of the finite-difference Malthus equation. Research results and their discussion. With the use of GIS technologies the study determined the composition, analyzed the structure and spatial development of the urban agglomeration. It has been revealed that it is one of the fastest growing in Russia. The natural population growth has maintained for a long time, and only last year it started declining. The main role in the formation of the population is played by migration of all types (international, interregional, intraregional). Demographically, at the intra-agglomeration level, the core and the nearest suburbs stand out clearly in the first place. The peripheral zone, which falls within the 1.5-hour zone of transport accessibility, has negative demographic trends. Conclusions. The conducted analysis made it possible to identify the demographic features of Krasnodar urban agglomeration. Demographically, there is a clear differentiation of the core, suburban and peripheral zones. The population is growing rapidly in the core of the agglomeration and the nearest suburb. In some suburban settlements, the growth rate is higher than in the core. According to the scale of the migration growth, the agglomeration is in the top three agglomerations of Russia, second only to the capital. The demographic prospects of the agglomeration are highly optimistic. In the future, the population growth will continue, but their volume will gradually decrease due to the increasing negative trends in the demographic situation in Russia.

An open source delineation and hierarchical classification of UK retail agglomerations

Town centres and high streets typically form the social and commercial cores of UK cities and towns, yet, there is no uniform definition of what a town centre or high street is. In this study the spatial delineations of retail agglomerations are generated using open-source data for England, Wales, Scotland and Northern Ireland. The extent and boundaries of these physical retail areas are identified based on the density and connectivity patterns of individual retail units over space. A high resolution hexagonal grid is superimposed over spatial clusters of retail points and a network-based algorithm used to identify mutually exclusive tracts. Agglomerations are then pruned and fine-tuned according to a series of heuristic rules. Our retail agglomerations represent local commerce areas with shopping amenities and are assigned to a hierarchical classification ranking from the largest Regional Centres, Major Town Centres and Town Centres, down to Small Local Centres and Retail Parks. The classification into one of eleven hierarchies is based on a combination of relative rank in the local area and absolute size of retail units within the area. These retail agglomeration boundaries, hierarchical classification and lookups form an open-source spatial data product available for wide use and research implementation.

Using Tencent User Location Data to Modify Night-Time Light Data for Delineating Urban Agglomeration Boundaries

The study of urban agglomeration boundaries is helpful to understand the internal spatial structure of urban agglomeration, evaluate the development level of urban agglomeration, and thus, assist in the formulation of regional planning and policies. However, previous studies often used only static spatial elements to delineate the boundaries of urban agglomerations, ignoring the spatial connections within urban agglomerations. In this study, night-time light and Tencent user location data were evaluated separately and fused to delineate urban agglomeration boundaries from both static and dynamic spatial perspectives. Additionally, it has been shown in the study results that the accuracy of urban agglomeration boundary delineated by night-time light data is 84.90%, with Kappa coefficient as 0.6348. The accuracy delineated by Tencent user location data is 82.40%, with Kappa coefficient as 0.5637, while the accuracy delineated by data fusion is 92.70%, with Kappa coefficient as 0.7817. Therefore, it can be concluded that the fusion of night-time light and Tencent user location data had the highest accuracy in delineating urban agglomeration boundaries, which verified that the fusion of dynamic spatial elements on a single static spatial element can supplement the spatial connection of urban agglomeration. Our findings enrich the understanding of urban agglomerations, and the accurate delineation of urban agglomerations boundaries can aid urban agglomeration planning and management.

АГЛОМЕРАЦИОННЫЕ СЮЖЕТЫ В СТРАТЕГИЯХ МУНИЦИПАЛЬНЫХ ОБРАЗОВАНИЙ ЛЕНИНГРАДСКОЙ ОБЛАСТИ

The purpose of the study is to measure the level of «agglomeration» of municipal strategies of the Leningrad region, that is, the depth of reflection in the context of the strategy of understanding the significance of inclusion in the composition or being in the zone of influence of the St. Petersburg agglomeration. The study has been conducted on the example of municipal districts and settlements of the Leningrad region. A list of eight «agglomeration strategies» and a method of expert scoring evaluation of the manifestation of each scenario in the strategy text have been developed. A three- component methodology for assessing the degree of «agglomeration» of the strategy has been imple- mented. The results show that the «agglomeration» of the strategy lessen with increasing distance from St. Petersburg. Nevertheless, the strategy of the Kirovsky district, which is part of the agglomeration, received the maximum assessment. This can be explained by the fact that the «agglomeration» of strat- egies also depends on the consultants involved in the development of strategies. Municipal strategies of the Leningrad Region were created with the participation of such organizations as Urbanica, Region- Expo, Higher School of Economics and ENKO. Among the eight identified «agglomeration strategies», «Industrial connectivity», «Demography and mobility» and «Transport connectivity» appeared to be more common and better worked out. The scenarios «Infrastructure and Finance» and «Ecology» are less frequent and manifested worse. «Agglomeration» has a predominantly positive connotation in the strategies of the districts close to St. Petersburg, while the connotation in the strategies of the peripheral districts of the region is mostly negative. When updating municipal strategies of the Leningrad region, it is recommended to increase the degree of integrity of the «agglomeration strategies» consideration; take into account forecasts concerning the dynamics of the St. Petersburg agglomeration boundaries and synchronize the development of strategies at different levels.

Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion—A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area (GBA)

The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies.

Methodology for Identifying the Boundaries of Agglomerations based on Statistical Data

Significant difference in development between the municipalities is an obstacle for achieving economic sustainability in many Russian regions. Regional development strategies and plans of various municipalities often cannot be synchronised because of their incompatibility. Moreover, municipalities usually lack necessary resources to implement their strategies. To solve these problems, we identified groups of municipalities (key units of the settlement system (KUSS)) based on the existing socio-eco- nomic relations, common challenges and development potential. We propose a methodology for identifying KUSS and describe its application. This methodology relies on statistical data available at the municipal level. To assess the interconnection of municipalities, we used 6 statistical socio-economic indicators and 1 integrated index of Gross Municipal Product (GMP). The difference in the first 6 indicators of the development of municipalities demonstrated, that less developed municipalities tend to more developed ones. We used the values of gross municipal product to define high mutual attraction of large municipalities (similar to the gravity model). The distance between municipalities reduced their mutual influence. Due to the limited data set, it was necessary to consider the reliability of the identified inter-municipal relations. Thus, we compared the obtained results with empirical data on population distribution and circular migration between municipalities in the region. The comparison of our calculations and actual data showed high precision of the presented methodology. The resulting grouping of municipalities allowed identifying 21 key units of the settlement system in Sverdlovsk oblast. The proposed methodology can be used for deter- mining large groups of municipalities in Sverdlovsk oblast, as well as in other Russian regions.

Геоинформационное обеспечение изучения размещения зарубежных ТНК в глобальных городах

The article discusses the experience of assessing the location of foreign TNCs in global cities as a significant factor in the field of environmental, economic and social aspects of sustainable development of urban areas. A number of initial theoretical models are proposed from the standpoint of the center-peripheral approach and a conceptual scheme of geoinformation and cartographic support of the study is presented, the main stages of its implementation are revealed. The databases of the European Commission’s Joint Research Center (GHS Urban Center Database, GHS-UCDB) and (GHS Functional Urban Areas, GHS-FUA) were used to get an idea of the real models of the intracity location of large business in the delimitation of the boundaries of agglomerations and their separate zones. To determine the composition of TNC divisions, Forbes 2000 data for 2020 and information from corporate websites on the branch network with geographic reference from the Craft.co reference service were used. In total, 55 global cities of the alpha group and more than 12 thousand foreign branches of TNCs were involved in the analysis. The processing and systematization of the obtained data was carried out using the object-relational database management system (RDBMS) PostgreSQL and the PostGIS extension. The visualization of the research results based on the consolidation of tabular data and spatial relationships using QGIS. Comparative analysis showed the effectiveness of the developed technique. As a result of its application, the overall scale of the presence of big business in the global cities of the alpha group and a significant gap between leaders and outsiders were determined. The absence of a universal model for the zonal distribution of TNCs in the urban space was revealed, the basic invariants of which were visualized using the example of a number of centers. The presented developments can contribute to the development of the direction of microgeographic research in geo-urban studies. In practice, the methodology can be useful for modernizing the assessment of the quality of the urban environment and the conditions for its formation as part of the implementation in the Russian Federation of the national project “Housing and Urban Environment”.

Methods and approaches of determining the boundaries of agglomerations (basic case Kharkiv region)

Changes in social relations in Ukraine, its transition to a post-industrial society, structural changes in regional development lead in modern conditions to changes in the territorial organization of society, which directly affects the trends of regional development. The highest form of territorial organization of society are agglomerations – uban agglomerations. Defining the boundaries of agglomerations is an important step in the process of studying agglomerations and managing regional development, because it is necessary to clearly understand the territory to which the administrative influence should extend and the number of city councils whose activities should be coordinated in implementing regional policy. The purpose of this work is to analyze the existing methods, approaches and techniques for determining the boundaries of agglomerations, development of an algorithm for establishing the boundaries of agglomerations and its testing on the materials of the Kharkiv region. The main theoretical and methodological starting points of delimitation of agglomeration boundaries are presented, approaches, methods and techniques, their quantitative and qualitative criteria, indicators, etc. are determined. The author 's algorithm for establishing agglomeration boundaries is substantiated, initial positions, key indicators, coefficients and indices are indicated. The proposed algorithm was tested on the materials of Kharkiv region. It is established that the Kharkiv agglomeration is monocentric and well formed. The development coefficient of the Kharkiv urban agglomeration is 30.7, the agglomeration coefficient is 0.13, the agglomeration index is 0.25, which confirms the high level of development of this agglomeration and the significant polarizing effect of the city of Kharkiv. The applied indicators of concentration and population density, transport accessibility, allowed to establish that the Kharkiv agglomeration has a powerful organizational center of development - the city of Kharkiv, the polarizing effect of which is more pronounced in two districts – Kharkiv and Dergachiv, which form the central zone of the agglomeration. The center of the agglomeration is connected with remote centers of peripheral areas by radial-radial connections by transport and other types of connections, which forms an external zone consisting of Bohodukhiv, Valkiv, Vovchansk, Zmiiv, Zolochiv, Novovodolazk, Pecheneg and Chuhuiv districts. Based on the study, based on the principle of superposition, the author's vision of delimitation of the Kharkiv agglomeration in 2019 is presented. The zone of influence of organizational functions of the center extends to a radius of 70 km, but there are significant territorial disparities in the center and periphery. The city of Kharkiv assumes most of the functions: administrative, economic, social, infrastructural and recreational, which to some extent hinders the development of small towns that are part of the agglomeration. The strengths of the Kharkiv agglomeration are indicated, as well as the priority directions of development are singled out. Given that the Kharkiv agglomeration is monocentric, and within it, as in the region there is a sharp uneven development of the center and periphery, effective management and addressing the hypertrophied development of the city, spatial planning and infrastructure development will alleviate this situation and facilitate the transition of the region. to the polycentric model of spatial organization, respectively, and the reduction of territorial disparities in the level and quality of life of the population of the Kharkiv region.

Геоинформационный анализ агломерации Кавказских Минеральных Вод: градостроительные и функционально-планировочные аспекты

The article presents the results of research on the formation and development of the functional and planning structure of the urban agglomeration of the Caucasian Mineral Waters, as an integral part of the settlement system of the Stavropol territory and the North Caucasus Federal district. Theoretically, the authors rely on the concept of a single settlement system and a reference frame as a combination of nodal and linear elements that Express the main features of the territorial organization of the region. The paper uses various sources of quantitative and qualitative information on the Stavropol territory as a whole, the region of Caucasian Mineral Waters and individual municipalities: results of sociological surveys of the population, census data, information about the current socio-economic and demographic situation of the territory, materials of state and departmental statistics, documents of territorial planning and urban planning, own observations and results of “field” studies of the territory. Based on these data, a General characteristic of the CMS agglomeration is given, the spatial organization of settlement is analyzed, and current trends in its development are determined. One of the results of the study is a geographic information model of the agglomeration, which sets its boundaries and composition, calculates the coefficient of development, and makes a demographic forecast for the agglomeration as a whole and for individual municipalities. The model of spatial organization of the territory was based on the planning framework of the region, including social, economic, environmental, and transport frameworks. The territory of the agglomeration, despite its high level of formation, differs in significant contrasts, which makes it possible to distinguish different zones within it by the level of urban development and the role in the agglomeration. Using the methods of geoinformation mapping, maps of the agglomeration boundaries and transport accessibility of its cores were created, as well as a perspective structural and planning model of the CMS region. The authors come to the conclusion that the design decisions for the development of territories within the CMS agglomeration, given in the documents of territorial planning and urban planning of municipalities, should be based on the main function of the region — ecological and resort, which gives it the status of a region of Federal significance. Therefore, the measures set out in the territorial planning documents of each municipality must be interconnected (both in terms of hierarchical subordination and in the General context of agglomeration development of the territory) for the sustainable development of municipalities and the entire agglomeration, the preservation of the environment and cultural heritage.

Algorithm of allocation of territories included to agglomerations

The article covers topical issues of formation, development and functioning of agglomeration formed around large cities. A number of issues requiring further research have been identified, in particular, the definition of the essence of the concept of “agglomeration”, the criteria for the allocation of agglomerations, its recognition in legislative and regulatory documents and the methods of delimitation of agglomerations. The algorithm of allocation of territories included to agglomerations has been suggested. The proposed algorithm for the allocation of territory, included in the agglomerations, will allow allocating agglomeration territories in a system way. The authors identify the existing boundaries of agglomerations and provide proposals for its further development.

Boundary delimitation of Chelyabinsk agglomeration

The article is devoted to the theme of boundary delimitation of Chelyabinsk city agglomeration. For this purpose, a method of determining functional urban areas was used. This methodology is international and has been applied repeatedly for definition of agglomeration boundaries around the world. Applying this method makes it possible to compare obtained results with other world analogs of agglomerations, and also to understand the nature of development of agglomeration under study. The result of the study is a map with marked boundaries of agglomerations and a list of municipalities that entered zone of the core city influence. It was concluded that there is a significant degree of centralization of Chelyabinsk agglomeration (75% of the labor force) and an incomplete formation of the suburbia. Satellite cities and areas of their influence on suburban areas were identified. This study was a part of architectural and planning section of the research paper "A Strategy for the Development of Chelyabinsk agglomeration," carried out in 2018. The study helped to understand and trace distribution structure of urbanized areas within the agglomeration, to focus on compactness of the settlement system and to exclude proposals for excessive expansion of agglomeration boundaries by including agricultural municipalities.

Boundary delimitation of Chelyabinsk agglomeration

The article is devoted to the theme of boundary delimitation of Chelyabinsk city agglomeration. For this purpose, a method of determining functional urban areas was used. This methodology is international and has been applied repeatedly for definition of agglomeration boundaries around the world. Applying this method makes it possible to compare obtained results with other world analogs of agglomerations, and also to understand the nature of development of agglomeration under study. The result of the study is a map with marked boundaries of agglomerations and a list of municipalities that entered zone of the core city influence. It was concluded that there is a significant degree of centralization of Chelyabinsk agglomeration (75% of the labor force) and an incomplete formation of the suburbia. Satellite cities and areas of their influence on suburban areas were identified. This study was a part of architectural and planning section of the research paper "A Strategy for the Development of Chelyabinsk agglomeration," carried out in 2018. The study helped to understand and trace distribution structure of urbanized areas within the agglomeration, to focus on compactness of the settlement system and to exclude proposals for excessive expansion of agglomeration boundaries by including agricultural municipalities.

Methods for spatial analysis of city structure distribution to estimate city agglomeration boundaries

Abstract The paper outlines the development of city structure spatial analysis methods to address a series of applied issues in developing the efficient transportation systems for city agglomerations. The interest of a wide community of researchers is focused on the acquisition of initial data to generate a model of transportation demand in city agglomerations based on an analysis of land-use and territory building-up, mainly within the city agglomeration center — urban district area. The paper covers the potential of applying methods for a spatial analysis of city structure distribution to obtain basic parameters of population mobility such as the probability of transportation. In particular, an algorithm of developing Box-Cox-type function is given. The function describes transport preferences of city agglomeration residents, the probability of trips for various purposes depending on total time and financial expenditures. The equations obtained are used to develop a transportation demand model based on the distribution density of city structure elements. A formalized definition of the city agglomeration concept is given. It is proposed to establish city agglomeration boundaries based on an analysis of the distribution density of city property and on an analysis of uneven loading of the main transport links outside the city agglomeration center. A procedure of establishing the boundaries for a typical uni-centric city agglomeration is described using the Perm city agglomeration. The approach proposed can be applied both in combination with generating optimization and forecast transport models of cities and city agglomerations and independently, at the first stage of collecting initial data on the intensity of transport and passenger flows in the area affected by the city agglomeration.

Moscow Urban Heat Island: Detection, boundaries, and variability

The determination of the anthropogenic impact on the thermal regime of a megalopolis is discussed. The nominal boundaries of the megalopolis thermal effects are determined taking into account orographic and landscape effects and the variability of temperature difference between the center of the city and its suburbs. It is proposed to use the data of stations located within 40-50 km from the urban agglomeration boundaries for calculating the anthropogenic component of the urban heat island. The parameters of the heat island in Moscow are determined from the measurement data on maximum and minimum daily surface air temperature in 2012-2016, and their significant seasonal and intradaily variability is revealed. It is corroborated that anthropogenic causes for the higher air temperature in Moscow as compared with suburbs during the cold season are the release and loss of heat of heating systems; in the absence of snow cover the anthropogenic effect of these sources is enhanced by the thermal radiation of the urban surface.

Regional Business Competitiveness: Medium and Low-Technology Production Systems in Northern Greece

1. Introduction The following analysis focuses on the vulnerable region of Northern Greece, where the bulk of the country's traditional (4) manufacturing is concentrated in local production systems of small and medium-sized manufacturing enterprises. The paper attempts to estimate the impact of technological and innovation dynamism on regional business competitiveness under the current conditions of globalisation and crisis. To this purpose, technological and innovation capacity is explored across enterprises of different industries in the region concerned. Industrial/sectoral and territorial/localized factors are taken into account, as determinants of competitive business strategies. Such factors encompass the technological level of the regional production system, the pre-established industrial structures, the labour market, human capital and the institutional basis. We assert that entrepreneurial technological capacities are the outcome of production, social and institutional structures of the regional context. The analysis focuses on manufacturing sectors of high labour intensity and low or medium technology intensity, operating in the regions of Western, Central and Eastern Macedonia and Thrace. Primary and secondary data are analysed on the business and sector level, in order to estimate the impact of sectoral and territorial factors on entrepreneurial technological capacity and consequently, regional competitiveness. The main objective of the analysis is to contest mainstream microeconomic approaches that advocate the dependence of competitiveness merely on production cost. The paper attempts to substantiate that the adoption of dynamic strategies improving productivity through technological advancement and innovation is the prerequisite for business and regional competitiveness. A central hypothesis in the following analysis is that upgraded technological capacities have a positive contribution to firms' economic performance. Thus, business data on employment, labour skills, practices for technological improvement and innovation are associated to data on sales, in order to provide indication of entrepreneurial competitiveness. Results indicate that 'passive' business strategies compressing production cost, wages and investments, result into low value added production and weak competitiveness. While, higher competitiveness is achieved through 'reactive' strategies motivating business potential in the direction of technological upgrading and innovativeness. This of course requires skilled labour, high wages and substantial investments that allow for high value added production. 2. Regional development and inequalities in the New Economy of Knowledge According to the New Economy theory, firms, industries, regions and economies that learn faster become more competitive as the knowledge they develop particularly the one embodied in labour and social capital--is hardly imitated or transferred to competitors (Storper, 1997). As knowledge has become increasingly traded, growth inequalities are the outcome of the uneven capacities of economic agents to absorb it and transform it to innovation (Hudson, 1999). Likewise, the successful integration of regional agglomerations in international networks is based on their capacity to accumulate knowledge and innovate. In Romer's endogenous growth theory (Romer, 1986, 1990), knowledge spillovers and their externalities created within the spatial boundaries of agglomerations constitute important sources of innovation and lead to increasing development rates. The related discourse focuses on the connection of innovativeness to the industrial structure/composition of regional agglomerations and explores whether local externalities affect differently innovativeness in industrial sectors of different technological intensity (Glaeser et al., 1992; Greunz, 2004; Thalassinos et al., 2012). This connection will be explored in the following analysis as well. …

The 2008 financial-economic crisis and spatial transformation of the St. Petersburg agglomeration

The paper analyzes the degree and directions the 2008 global financial-economic crisis has had on the land transformation processes in the St. Petersburg monocentric agglomeration. It considers the approaches to definition of agglomeration boundaries and notes that the crisis has greatly affected the functioning of the St. Petersburg agglomeration. The examined spheres of life activity include land development, population migration, the labor market, investment activity, and federal and municipal governance. The major conclusion is that the crisis has intensified the agglomeration processes.

Le Caire : les problèmes de la croissance à la lumière du recensement de 1986

According to the 1986 Census, the population in Cairo has reached 10 million inhabitants. With a built-up area of about 32000 hectares, it's one of the most densely populated city in the world. The increase remains at a high level (2, a year), but is now almost natural. This new fact is due mainly to the stagnation of urban growth at national level. As a result of huge public investments in the field of urban utilities, living conditions have improved, while informal dynamics, together with a rather ancient process of overdensification, may have caused a reduction of the housing shortage. However urban expansion has been pursued at the expense of arable land, with an annual loss of 340 hectares within the agglomeration boundaries. Another major trend of the last years is the increase of vacant housing units (17% of total stock) on the one hand and of shanty dwellings on the other. This phenomenon shows the inadequate relation between supply and demand in such a field, as well as the growing pressure of the speculation process. Now, in a context of economic recession, this qualitative crisis may have dramatic consequences, both at national level (greater exclusion of lower income population and increase of unsound settlements).

Delimination of Boundaries of the Agglomeration of Ústí n/L.

The author of this paper tries to explain on an example the use of a general coefficient for the delimitation of boundaries of agglomerations. He discussed this theme already previously in two articles which were published last year. In case the optimum number of data are available, the general coefficient may distinguish the following 10 characteristic qualities of individual housing estates: 1. position 2. possibility of connection with other places by some public means of transport 3. economical structure (proportion of non-agricultural population) 4. transportation of working people to the centre of the agglomeration 5. transportation of working people to some other place 6. increase in number of population 7. density of population and the quality of technical equipment of the housing estate 8. horizontal extension of the housing estate 9. number of social facilities (shops, schools, public health centres, etc) 10. the deviation from a typical centripetency As the example the author chose the town of Ústí n/L which is the large at town agglomeration in north-west Bohemia. Map 1 shows three stages in the development of the agglomeration: a) before the growth of industrialisation (1830) b) at the beginning of industrialisation (1854) c) in the period of progressing industrialisation (1900) The author describes in much detail the influence of the terrain upon the direction of growth of the agglomeration. In this case, the most important factor was the canyon of the Elbe. At the present time, another important agent comes into being, i. e. the progressing open coal-mining which will hinder the extension of the agglomeration in the south-west and west, partly also in north-west direction. In last years, the agglomeration kept on growing in this direction (Všebořice) but at the present it started growing to the north. The situation is shown in cartogram 3 (increase in number of population in 1950-61; the line indicates preliminary working boundary). It should serve as example of a more effective representation than the one used up to now (2 indicates economical structure). Map 4 indicates the transport facilities by some public means of transport (the width of the zone marks frequency, its length proportionality between 5 a. m. and 10. p. m.). Fig. 1 shows the growth of the agglomeration since 1830. A further map gives figures of the complex coefficient (consisting of eight parts, with no statistical data available for its forth and fifth part as yet) for 30 suburban districts out of which 15 - according to the opinion of the author - are to be included into the agglomeration. The last map shows a general classification of the housing estates together with the present administrative (dotted) as well as the suggested agglomeration boundary. In the conclusion, the author treats of the prospective development of the agglomeration. Its north-western part will most probably go on extending, meanwhile its eastern part - the largest at the present - will be reduced by two villages (Mojžíš and Veselí situated more to the east, owing to an ever-increasing influence of the neighbouring industrial agglomeration of Povrly.