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In this paper, a comprehensive research of the evolution of the hierarchical structure and spatial pattern of coastal cities in China was conducted based on the data of distribution of the headquarters and subsidiaries of marine-related enterprises in 1995, 2005 and 2015 using the city network research method proposed by Taylor. The results of the empirical research showed: China’s coastal city network had an obvious hierarchical characteristics of “national coastal cityregional coastal city-sub-regional coastal city-local coastal city”, in the 20 years of development process, the hierarchies of coastal cities in China showed a hierarchical progressive evolution; in past 20 years, the spatial pattern and network structure of coastal cities in China tended to be complete, and the city network was more uniform, forming a “three tiers and three urban agglomerations” network structure; the strength of connection among the cities was obviously strengthened, and the efficiency of urban spatial connection was improved overall.

. Conserv. Biol. 20(5): 1487-1498. Nathan A. & Muller-Landau H. C. 2000. Spatial patterns of seed dispersal, their determinants and consequences for recruitment. Tree 15(7): 278-285. Pearson R. G. & Dawson T. P. 2005. Long-distance plant dispersal and habitat fragmentation: identifying conservation targets for spatial landscape planning under climate change. Biol. Conserv. 123(3): 389-401. Pyšek P., Jarošik V. & Kučera T. 2002. Patterns of invasion in temperate nature reserves. Biol. Conserv. 104: 13-24. Richardson D. M. & Pyšek P. 2006. Plant invasions: merging the

] Wollni, M. & Andersson, C. (2014). Spatial patterns of organic agriculture adoption: Evidence from Honduras. Ecological Economics 97, 120-128. DOI: 10.1016/j.ecolecon.2013.11.010. [36] Xingbai, X. & Lee L. (2015). Maximum likelihood estimation of a spatial autoregressive Tobit model, Journal of Econometrics 188(1), 264-280. DOI: 10.1016/j.jeconom.2015.05.004. [37] Zimmermann, A. & Heckelei, T. (2012). Structural Change of European Dairy Farms - A Cross-Regional Analysis. Journal of Agricultural Economics 63(3), 576-603. DOI: 10.1111/j.1477-9552.2012.00355.x. [38] CEAS

modelling approach to simulate preferential flow and transport in water repellent porous media: Model structure and validation. Australian Journal of Soil Research, 43, 361–369. Schlesinger, W.H., Raikes, J.A., Hartley, A.E., Cross, A.F., 1996. On the spatial pattern of soil nutrients in desert ecosystem. Ecology, 77, 364–374. Taiz, L., Zeiger, E., Møller, I.M., Murphy, A., 2015. Plant Physiology and Development. 6th Edition. Sinauer Associates, Inc, Sunderland, Massachusetts, USA, 761 p. ISBN: 978-1-60535-255-8 Täumer, K., Stoffregen, H., Wessolek, G., 2005

Bohemia. Forest Systems, 20:122–138. Bulušek, D., Vacek, Z., Vacek, S., Král, J., Bílek, L., Králíček, I., 2016: Spatial pattern of relict beech ( Fagus sylvatica L.) forests in the Sudetes of the Czech Republic and Poland. Journal of Forest Science, 62:293–305. Burrascano, S., Sabatini, F. M., Blasi, C., 2011: Testing indicators of sustainable forest management on understorey composition and diversity in southern Italy through variation partitioning. Plant Ecology, 212:829–841. Clark, P. J., Evans, F. C., 1954: Distance to nearest neighbour as a measure of spatial

floristic variation in rainforest ferns. Journal of Ecology , 94: 181–195. K arunaratne , S., S ingh , B., R obinson , L., C ampbell , C., Y ao , H., P owell , J., 2015. Deterministic processes vary during community assembly for ecologically dissimilar taxa. Nature Communications , 6 (1): 1–10. K ing , A. W., W ith , K. A., 2002. Dispersal success on spatially structured landscapes: when do spatial pattern and dispersal behavior really matter? Ecological Modelling , 147 (1): 23

). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure [computer software] . Portland, USA: Department of Agriculture, Forest Service, Pacific Northwest Research Station. Miller, J.N., Brooks, R.P. & Croonquist M.J. (1997). Effects of landscape pattern on biotic communities. Landsc. Ecol. , 12(3), 137-153. DOI: 10.1023/A:1007970716227. Mišovičova, R. & Pucherova Z. (2008). The classification of Nitra´s town contact area and selected villages in its hinterland. Problemy Ekologii Krajobrazu , 20, 299-305. Moreira, F., Queiroz, I. & Aronson J

MISCELLANEA GEOGRAPfflCA WARSZAWA 2 0 0 0 Vol. 9 Elzbieta Kupczyk, Roman Suligowski TEMPORAL AND SPATIAL PATTERNS OF HIGH INTENSITY RAINFALLS IN POLAND RAINFALL DEPTH OF SPECIFIED RETURN PERIOD In many situations of hydrological practice as design floods estimation at ungauged sites or drainage system projects, the design storm serves as the input element for runoff hydrograph calculation. A design hyetograph speci- fies not only a rainfall event depth (or intensity), duration and frequency but also gives the time distribution of rainfall depth during a


Selected traits of the spatial organisation of a geographical environment which stem from two types of human behaviour (locational and interactive) are examined in this paper. An attempt is made to find and account for similarities in the spatial patterns of scalar and vector geographical data. In doing so, the paper analyses a core-periphery dichotomy, based on socio-economic information, and travel-to-work patterns. The paper uses the concept of a region as an integrating and focusing framework for the study. Formal regions (peripheral areas) are defined through the application of principal components analysis and cluster analysis; functional regions are defined by a standard rule-based regionalisation algorithm. The territory of the Czech Republic is used as an area for testing the basic hypotheses. The results show that there is some form of interrelationship and complementarity between the spatial distribution of scalar data and vector data, i.e. between spatial structure and spatial interaction patterns, which together form the spatial organisation of a geographical environment.


Using hierarchical cluster analysis there were distinguished six spatial patterns of bird communities during the breeding season in the Lower Dnieper Sands. The differentiation of these patterns is based on a spatial heterogeneity in the area ratio of different habitats. The sites with natural and sub-natural landscapes hold three types of bird communities. Another type of the bird community is relatively similar to the previous three, but characterized by a poor quantitative and qualitative bird composition; it is associated with open landscapes with destroyed natural vegetation. Bird communities of artificial pine plantations (the most transformed landscapes of the Lower Dnieper Sands) are very different from those in the natural and sub-natural landscapes. The spectra of ecological groups of different bird communities match the spectra of different habitat types obtained using a supervised classification of remote sensing data. It makes it possible to use a topological model of the habitat types (based on remote sensing data) as a predictor for GIS modelling of spatial distribution of different birds communities throughout the Lower Dnieper Sands.