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Journal articles on the topic 'Spatial distribution'

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Published: 4 June 2021

Last updated: 6 September 2023

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1

Караханян, Ашхен, Ashhen Karakhanyan, Сергей Молодых, and Sergey Molodykh. "Spatial distribution of temperature during geomagnetic disturbances." Solar-Terrestrial Physics 4, no. 4 (December 21, 2018): 59–62. http://dx.doi.org/10.12737/stp-44201808.

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We propose an index of efficiency of the solar activity effect on the tropospheric temperature, which takes into account the spatial irregularity of the response to this effect. As a proxy of solar activity we take the PC index of geomagnetic activity, designed to monitor the geomagnetic field at high latitudes. Using NCEP/NCAR reanalysis data, we carry out a comparative analysis of variations in the proposed index and lower-troposphere temperature variations during geomagnetic disturbances. We identify the presence of a high degree of correlation between the temperature in the 925–700 hPa layer and the proposed index of solar activity effect. The spatio-temporal analysis of the index and temperature variations shows that the index of effi-ciency of the solar activity effect describes well both the value and the sign of the observed variations in the spa-tial distribution of the lower-troposphere temperature as compared to the frequently used index of geomagnetic activity.

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2

Murtadho, Alfin, Andrea Emma Pravitasari, Khursatul Munibah, and Ernan Rustiadi. "Spatial Distribution Pattern of Village Development Index in Karawang Regency Using Spatial Autocorrelation Approach." JURNAL PEMBANGUNAN WILAYAH & KOTA 16, no. 2 (June 30, 2020): 102–11. http://dx.doi.org/10.14710/pwk.v16i2.24883.

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Jabodetabek and Greater Bandung Metropolitan areas experiencing the phenomenon of urban expansion which has caused both metropolitan areas to be more connected and become a mega-urban area by a corridor through a conurbation process. Karawang regency is part of the region in the Jakarta-Bandung corridor. The increasing number of population in the region will encourage the addition of number and types of public facilities. The objectives of this study were to analyze the regional development and spatial distribution pattern of the Village Development Index/ Indeks Perkembangan Desa (IPD) in Karawang regency. Regional development was analyzed using scalogram analysis, while spatial distribution pattern of the IPD was analyzed using Global and Local Moran Index. Regional development analysis shows the distribution of the IPD in 2014 has increased from 2003. Analysis of spatial distribution pattern of IPD values from 2003 and 2014 shows the clustered distribution patterns in some sub-districts.

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3

Ertan, Hayri. "Exploratory spatial analysis of hit distribution in archery." International Journal of Academic Research 5, no. 6 (December 10, 2013): 112–18. http://dx.doi.org/10.7813/2075-4124.2013/5-6/a.15.

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4

Cardina, John, Gregg A. Johnson, and Denise H. Sparrow. "The nature and consequence of weed spatial distribution." Weed Science 45, no. 3 (June 1997): 364–73. http://dx.doi.org/10.1017/s0043174500092997.

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Seed dispersal, interacting with environmental disturbance and management across heterogeneous landscapes, results in irregular weed spatial distributions. Describing, predicting, and managing weed populations requires an understanding of how weeds are distributed spatially and the consequences of this distribution for population processes. Semivariograms and kriged maps of weed populations in several fields have helped describe spatial structure, but few generalizations can be drawn except that populations are aggregated at one or more scales. Limited information is available on the effect of weed arrangement, pattern, or field location on weed population processes. Because weeds are neither regular nor uniform in distribution, mean density alone is of limited value in estimating yield loss or describing population dynamics over a whole field. Sampling strategies that account for spatial distribution can increase sampling efficiency. Further research should focus on understanding processes that cause changes in spatial distributions over time to help predict rates of invasion and potential extent of colonization.

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5

Chu, Yaoquan, and LiZhi Fang. "Spatial Distribution of Quasars." Symposium - International Astronomical Union 124 (1987): 627–38. http://dx.doi.org/10.1017/s0074180900159650.

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The distribution of quasars has become one of the most interesting problems in observational cosmology. This is due mainly to the development of theory of the formation of large scale structure in the universe. In recent years, several scenarios of clustering have been proposed. In the adiabatic case, the clustering process is from larger scales to smaller ones, i.e., the first systems to form out would be on the scale of superclusters, then these systems fragment to form smaller scale systems such as galaxies. In the isothermal case, the clustering is from smaller scales to larger ones, namely, galaxies condense out at first and larger scale systems, such as clusters and superclusters, then form later via hierachical build-up processes. In the universe contain two components, the scenario of clustering might be different from both standard adiabatic and isothermal cases(1). According to this new scenario, there should be two kinds of small scale objects, one is formed due to fragment of larger scale systems, another is formed before large scale systems form.

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6

Malumian, V. H., and A. N. Harutyunyan. "Spatial Distribution of Pulsars." Astrophysics 47, no. 4 (October 2004): 568–72. http://dx.doi.org/10.1023/b:asys.0000049796.89898.01.

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7

Lioutas, Anestis, Gregory M. Smith, and Henk Jan Verhagen. "SPATIAL DISTRIBUTION OF OVERTOPPING." Coastal Engineering Proceedings 1, no. 33 (December 14, 2012): 63. http://dx.doi.org/10.9753/icce.v33.structures.63.

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The scope of this research is to find an empirical formula to describe the distribution of wave overtopping in the region behind the crest. A physical model was set up in which irregular waves were generated. In order to find a formula which adequately describes the test observations, the influence of several parameters has been analysed. The proper determination of the crest freeboard, which is a dominant factor, has been investigated. Finally, the test results have been used to assess and compare the existing relevant computational methods.

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8

Meyer, F. J., and D. K. Pradhan. "Modeling defect spatial distribution." IEEE Transactions on Computers 38, no. 4 (April 1989): 538–46. http://dx.doi.org/10.1109/12.21146.

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9

Nagatani, Akira. "Spatial distribution of phytochromes." Journal of Plant Research 110, no. 1 (March 1997): 123–30. http://dx.doi.org/10.1007/bf02506851.

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10

Fischer, Helmut, Martin Pusch, and Jürgen Schwoerbel. "Spatial distribution and respiration of bacteria in stream-bed sediments." Archiv für Hydrobiologie 137, no. 3 (September 5, 1996): 281–300. http://dx.doi.org/10.1127/archiv-hydrobiol/137/1996/281.

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11

Cabrol, Nathalie A. "Martian channels : Statistics of spatial distribution, relative datation, and implications." Zeitschrift für Geomorphologie 37, no. 1 (April 23, 1993): 57–75. http://dx.doi.org/10.1127/zfg/37/1993/57.

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12

Park, S. J., and T. P. Burt. "Spatial distribution of chemical weathering intensity on an acid hillslope." Zeitschrift für Geomorphologie 44, no. 3 (September 29, 2000): 379–402. http://dx.doi.org/10.1127/zfg/44/2000/379.

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13

Xu, Xu, Xu-Dong Gou, Sui Wan, Hang-Yu Liu, Hai-Bo Wei, Jian-Rong Liu, Jia-Hui Ding, et al. "Anomozamites (Bennettitales) in China: species diversity and temporo-spatial distribution." Palaeontographica Abteilung B 300, no. 1-6 (December 12, 2019): 21–46. http://dx.doi.org/10.1127/palb/2019/0067.

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14

Řehák, Jan. "Variability of Spatial Frequency Distribution." Geografie 95, no. 3 (1990): 186–94. http://dx.doi.org/10.37040/geografie1990095030186.

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A measure of spatial variability (called geostructural variance) is defined for a frequency distribution on a finite set of places in a space whose geographical relations are assessed by a matrix of (generally conceived) distances. A set of measures stemming from the same approach describe the positions and properties of individual places in the geostructure. This complex of characteristics provides a clear-cut way of an analytical diagnostic reflection of the spatial properties of frequency distributions.

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15

Ren, Qixin, Baoyan Shan, Qiao Zhang, and Changkuan Shui. "Influence of Urban Spatial Structure on the Spatial Distribution of Gaseous Pollutants." Atmosphere 14, no. 8 (July 31, 2023): 1231. http://dx.doi.org/10.3390/atmos14081231.

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The spatial distribution pattern of urban spatial structure affects air flow and local meteorological conditions, which in turn influence the diffusion of air pollutants. This study built the urban spatial structure index system based on DEM, urban road networks, and big data. The ordinary kriging interpolation method was used to analyze the spatial distribution of gaseous pollutant concentrations in Jinan City. Correlation analysis, stepwise regression analysis, and bivariate global spatial autocorrelation analysis were used to study the influence of the urban spatial structure index on the spatial distribution of gaseous pollutant concentration. The main conclusions were as follows: (1) Evident spatial and temporal differences were observed in the concentration distribution of gaseous pollutants in Jinan. The spatial distribution of NO2 and CO concentrations showed a gradual decrease from north to south. Spatial heterogeneity was observed in the distribution of SO2 and O3 concentrations. (2) The urban spatial structure indicators had varying effects on the spatial distribution of different gaseous pollutant concentrations. The important factors that influenced the spatial distribution of urban gaseous pollutant concentrations included terrain elevation, building density, building volume, and floor area ratio. The greater the terrain undulation, the denser the building distribution, the greater the difference in building volume, and the greater the plot ratio, the greater the impact on the diffusion and spatial distribution of urban gaseous pollutants. (3) The spatial distribution of urban gaseous pollutant concentrations was significantly affected by the urban spatial structure indicators in the surrounding areas. Furthermore, the spatial distributions of NO2, SO2, CO, and O3 concentrations had a significant negative spatial correlation with the average DEM and standard deviation of the surrounding adjacent areas and a significant positive spatial correlation with the average and standard deviation of building height, standard deviation of building area, and building density in the surrounding adjacent areas (in June).

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16

BOHAN, D. A. "Spatial structuring and frequency distribution of the nematode Steinernema feltiae Filipjev." Parasitology 121, no. 4 (October 2000): 417–25. http://dx.doi.org/10.1017/s0031182099006551.

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The frequency distribution of first generation, Steinernema feltiae Filipjev parasitic stages was over-dispersed with the majority of hosts containing few or no parasitic stages, whilst a few hosts contained a great many. Because of high extraction efficiency, the frequency distributions of the parasitic stages and the infective stages in the soil were assumed to be directly related. To explain the frequency distribution of the parasites it was therefore necessary to account for the frequency distribution of the S. feltiae infective stages in the soil. The infective stages were spatially aggregated into 30 cm diameter patches at the site of host death. These patches were randomly distributed approximately 1 m apart. At the 1 m scale, the pooled counts of infective stages were randomly distributed. Thus, in contrast to the frequency distributions, the spatial structuring of S. feltiae changed with the spatial scale of the interaction. This dynamic spatial structuring means that the majority of samples taken would contain few or no infective stages, whilst a few soil samples would contain a great many. Thus, the spatial structuring of the infective stages generates the over-dispersed frequency distribution of the S. feltiae in the soil. Hosts, encountering infective stages from this spatial distribution will, therefore, show an over-dispersed frequency distribution of S. feltiae parasitic stages.

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17

Hao, Qiushi, Benchen Fu, Yu Shao, and Liying Wang. "Distribution Characteristics and Spatial Reciprocity." Open House International 42, no. 4 (December 1, 2017): 44–51. http://dx.doi.org/10.1108/ohi-04-2017-b0006.

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This study aims to explore the spatial distribution characteristics and spactial reciprocity between industrial parks (IPs) and vocational education parks (VEPs): agglomeration density, functional matching, spatial organization efficiency, as well as space intensive utility. To achieve this objective, IPs and VEPs in urban centers of Jiangsu Province are selected as the objects of the study. First, spatial analysis of thermodynamic diagrams is employed in this study to qualitatively analyze the evolutionary characteristics of the spatial distribution of IPs and VEPs to explore the spatial aggregation characteristics of their clustering, integration, and comprehensive crossover. Second, a horizontal comparison of the data and indexes concerned reveals that areas with high agglomeration and functional matching exhibit a sound spatial reciprocity. Third, this study crystallizes the four structural prototpye paradigms formed during the reciprocity evolution between IPs and VEPs; it compares spatial organization efficiency, with the agglomeration–core structure ranking first, followed by the circle–core structure. Finally, SPSS is used to analyze the space intensive utility in order to verify the conclusions of qualitative analysis. The findings can comprehensively explain the regularities of the spatial distribution and reciprocity between IPs and VEPs. The findngs can also elucidate the design of regional industrial development and educational programs.

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18

Mahaki, Behzad, Neda Mehrnejat, Mehdi Zabihi, Marzie Dalvi, and Maryamsadat Kazemitabaee. "Spatial Distribution of Congenital Hypothyroidism." Archives of Iranian Medicine 24, no. 8 (August 1, 2021): 636–42. http://dx.doi.org/10.34172/aim.2021.90.

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Background: This study was designed and conducted to investigate the spatial distribution of permanent and temporary congenital hyperthyroidism (PCH and TCH) in Isfahan. Methods: This study was conducted on neonates who were born from March 21, 2006 to March 20, 2011 and had undergone the congenital hypothyroidism (CH) screening program in counties affiliated to the Isfahan University of Medical Sciences. CH was diagnosed in 958 patients who treated with levothyroxine. The incidence rates of permanent and temporary congenital hypothyroidism in Isfahan province were calculated and their distribution was shown on the map. The space maps were drawn using the ArcGIS software version 9.3. Results: Based on the data obtained from the screening program, the average incidence of congenital hypothyroidism in the province during the period of 2006–2011 was 2.40 infants per 1000 live births (including both PCH and TCH). The most common occurrence was in Ardestan County (10:1000) and the lowest overall incidence was observed in the Fereydounshahr county (1.39:1000). The incidence of PCH in the counties of Ardestan and Golpayegan had the highest rate in all years of study; and the greatest number of TCH cases in the five years were observed in Nain, Natanz, Khansar and Chadegan counties. Conclusion: Adding the time dimension and performing spatial-temporal analysis is suggested because of the following items: high prevalence of CH in Isfahan province, the important role of this disease in mental retardation and neuropsychiatric disorder, the necessity of conducting future medical researches to find possible factors of CH etiology in Isfahan province, as well as necessity of performing spatial analysis with advanced statistical methods.

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19

Hong, Ilyoung. "Spatial Distribution of Korean Geotweets." Journal of the Korean Cartographic Association 15, no. 2 (August 30, 2015): 93–101. http://dx.doi.org/10.16879/jkca.2015.15.2.093.

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20

Islam, Md Morshedul. "Spatial Distribution of Market Centers." International Research Journal of Business Studies 10, no. 3 (March 9, 2018): 135–46. http://dx.doi.org/10.21632/irjbs.10.3.135-146.

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21

Carlson, Carl A. "Spatial distribution of ore deposits." Geology 19, no. 2 (1991): 111. http://dx.doi.org/10.1130/0091-7613(1991)019<0111:sdood>2.3.co;2.

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22

Manoj, G., and P. Ray. "Spatial distribution of persistent sites." Journal of Physics A: Mathematical and General 33, no. 31 (July 28, 2000): 5489–500. http://dx.doi.org/10.1088/0305-4470/33/31/304.

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23

Lee, Ming-Long, and R. Kelley Pace. "Spatial Distribution of Retail Sales." Journal of Real Estate Finance and Economics 31, no. 1 (August 2005): 53–69. http://dx.doi.org/10.1007/s11146-005-0993-5.

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24

Coxon, C. E. "The spatial distribution of turloughs." Irish Geography 20, no. 1 (January 1987): 11–23. http://dx.doi.org/10.1080/00750778709478820.

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25

Baker, Rafael, and Dov Leshchinsky. "Spatial Distribution of Safety Factors." Journal of Geotechnical and Geoenvironmental Engineering 127, no. 2 (February 2001): 135–45. http://dx.doi.org/10.1061/(asce)1090-0241(2001)127:2(135).

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26

Rossi, Jean-Pierre. "Clusters in earthworm spatial distribution." Pedobiologia 47, no. 5-6 (January 2003): 490–96. http://dx.doi.org/10.1078/0031-4056-00218.

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27

WELLINGS, PAUL W. "Spatial distribution and interspecific competition." Ecological Entomology 12, no. 3 (August 1987): 359–62. http://dx.doi.org/10.1111/j.1365-2311.1987.tb01016.x.

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28

SANDSMARK, MARIA. "SPATIAL OLIGOPOLIES WITH COOPERATIVE DISTRIBUTION." International Game Theory Review 11, no. 01 (March 2009): 33–40. http://dx.doi.org/10.1142/s0219198909002194.

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The main objects here are Nash equilibria in spatial Cournot oligopolies when profits depend on coordinated distribution. Production is non-cooperative, but the subsequent transportation must be performed jointly to minimize costs. Cournot-Nash equilibria for this two-stage game with partial coalitional strategies are determined by means of a mathematical-based algorithm. A numerical illustration is presented.

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29

Araki, Makoto, Tsunenari Soeda, Hyung Oh Kim, Vikas Thondapu, Michele Russo, Osamu Kurihara, Hiroki Shinohara, et al. "Spatial Distribution of Vulnerable Plaques." JACC: Cardiovascular Imaging 13, no. 9 (September 2020): 1989–99. http://dx.doi.org/10.1016/j.jcmg.2020.01.013.

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30

Garcia, Pedro, Ana Benarroch, and Jose Manuel Riera. "Spatial distribution of cloud cover." International Journal of Satellite Communications and Networking 26, no. 2 (2008): 141–55. http://dx.doi.org/10.1002/sat.899.

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31

Venema, Henk W. "Spatial distribution of muscle fibers." Anatomical Record 241, no. 2 (February 1995): 288–90. http://dx.doi.org/10.1002/ar.1092410216.

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32

Yeshurun, Yaffa. "The spatial distribution of attention." Current Opinion in Psychology 29 (October 2019): 76–81. http://dx.doi.org/10.1016/j.copsyc.2018.12.008.

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33

Klimešová, D., and E. Ocelíková. "Spatial data modelling and maximum entropy theory." Agricultural Economics (Zemědělská ekonomika) 51, No. 2 (February 20, 2012): 80–83. http://dx.doi.org/10.17221/5080-agricecon.

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Spatial data modelling and consequential error estimation of the distribution function are key points of spatial analysis. For many practical problems, it is impossible to hypothesize distribution function firstly and some distribution models, such as Gaussian distribution, may not suit to complicated distribution in practice. The paper shows the possibility of the approach based on the maximum entropy theory that can optimally describe the spatial data distribution and gives  the actual error estimation. 

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34

Elek, A., N. Gyenge, M. B. Korsós, and R. Erdélyi. "Spatial Inhomogeneity in Solar Faculae." Proceedings of the International Astronomical Union 13, S335 (July 2017): 17–19. http://dx.doi.org/10.1017/s174392131701136x.

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AbstractIn this paper, we investigate the inhomogeneous spatial distribution of solar faculae. The focus is on the latitudinal and longitudinal distributions of these highly localised features covering ubiquitously the solar surface. The statistical analysis is based on white light observations of the Solar and Heliospheric Observatory (SOHO) and Solar Dynamics Observatory (SDO) between 1996 and 2014. We found that the fine structure of the latitudinal distribution of faculae displays a quasi-biennial oscillatory pattern. Furthermore, the longitudinal distribution of photospheric solar faculae does not show homogeneous behaviour either. In particular, the non-axisymmetric behaviour of these events show similar properties as that of the active longitude (AL) found in the distribution of sunspots. Our results, preliminary though, may provide a valuable observational constrain for developing the next-generation solar dynamo model.

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35

DIANA, NIDA, SONY NUGRATAMA, and Adrian Adrian. "Sebaran Lokasi Potensial Minimarket (Alfamart dan Indomaret) Di Kecamatan Babelan." Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 18, no. 1 (July 5, 2018): 62–71. http://dx.doi.org/10.21009/spatial.181.03.

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District Babelan has many minimarkets in the form of Alfamart and Indomaret. Minimarket in District Babelan amounted to 43 minimarket. Minimarkets are scattered in several villages in Babelan sub-district, namely Bahagia village, Kebalen village, Babelan Kota village, Kedung Pengawas village, Kedung Jaya village and Muarabakti village. The purpose of this study is to see the development of Alfamart and Indomaret, knowing the pattern of distribution as well as potential location of minimarket establishment. The method used in this research is quantitative descriptive analysis with survey approach. Methods of data analysis using stop-point analysis. Data collection using observation techniques by taking data on location and documentation. The results of this study indicate that the development of Alfamart and Indomaret in the District of Babelan experienced rapid development from 2013-2017. In 2013 in Babelan sub-district there are only 12 minimarkets and by 2017 it has increased to 43 minimarkets, which indicates that minimarket developments in Babelan Sub-district have progressed over time. Next, the pattern of distribution Alfamart and Indomaret in District Babelan has a pattern of clumping. Other than that, the determination of potential location of minimarket establishment is needed to know where the potential position to establish minimarket. The potential distance of minimarket between villages in Babelan sub-district is not much different. The average potential distance of minimarket between villages is 1,4 km. Keywods : Minimarket, distribution pattern potential location, District of Babelan

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36

Petitgas, Pierre, Mathieu Doray, Martin Huret, Jacques Massé, and Mathieu Woillez. "Modelling the variability in fish spatial distributions over time with empirical orthogonal functions: anchovy in the Bay of Biscay." ICES Journal of Marine Science 71, no. 9 (June 24, 2014): 2379–89. http://dx.doi.org/10.1093/icesjms/fsu111.

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Abstract Characterizing the space–time variability in spatial distributions as well as understanding its drivers is basic to designing robust spatial management plans. As a prerequisite, we analyse here how this variability relates to population dynamics in conjunction with environmental conditions. For that, spatio-temporal statistical approaches are needed but seldom used in fisheries science. To fill this gap, we showcase the usefulness of the method of empirical orthogonal functions (EOFs). Guidelines are given to apply the method on a series of gridded maps as derived from fisheries survey dataseries that now span over decades. The method is applied to the series, 2000–2012, of the spatial distributions of European anchovy in the Bay of Biscay at spawning time. Across the series, the EOF decomposition allowed to identify three main types of spatial distributions. One type corresponded to an extended distribution, another to a restricted distribution in core areas, and the third to a very coastal distribution. The coastal spawning distribution corresponded to a low population growth rate as it was never followed by a large recruitment in the subsequent year. We did not attempt to explain the spatial patterns per se but the drivers of change from one type of distribution to another. Stock size and fish size as well as bottom temperature and water column stratification were the covariates that controlled the variability in the spatial distributions over time. Further, the spatial distribution at spawning time related to recruitment in the following year, meaning that variability in the spatial distribution of spawning affected population dynamics. The typology of maps based on EOF decomposition summarized this spatial variability into spatial spawning configurations, which may serve spatial planning.

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37

Mazumder, Asit, and Mike D. Dickman. "Factors affecting the spatial and temporal distribution of phototrophic sulfur bacteria." Archiv für Hydrobiologie 116, no. 2 (July 18, 1989): 209–26. http://dx.doi.org/10.1127/archiv-hydrobiol/116/1989/209.

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38

Turčáni, M., and T. Hlásny. "Spatial distribution of four spruce bark beetles in north-western Slovakia." Journal of Forest Science 53, Special Issue (April 2, 2008): 45–52. http://dx.doi.org/10.17221/2157-jfs.

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Infestation density of four the most common spruce bark beetle species was estimated on 15 study sites (10 trees per site) in the Kysuce model region in 2006. Five half-metre long sections of the stem were selected and dissected at the base of the stem; midway between the base of the stem and the base of the crown; just below the base of the crown; in the middle of the crown; and in the upper part of the crown. The infestation density of bark beetles, expressed as the number of mating chambers per dm<sup>2</sup>, was determined. Ordinary kriging was then used to produce smooth maps and visualize spatial distribution of study species. Maps with isolines indicating high infestation were produced for study species (<I>I. Typographus</I> over 0.38; (<I>I. amitinu</I>s over 0.15; (<I>I. duplicatus</I> over 0.11; and <I>P. chalcographus</I> over 0.415 nuptial chambers per dm<sup>2</sup>). <I>Ips typographus</I> L. remained dominant species on majority of sites having high intensity of infestation. Lower altitudes in the south-eastern part of the region were often infested by <I>I. duplicatus</I> Sahlberg and also by <I>I. typographu</I> and <I>Pityogenes chalcographus</I> L. Higher elevations in the north-eastern part of territory in the vicinity of border with Poland were heavily infested by <I>I. amitinus</I> Eichhoff (often with <I>I. typographus). P. chalcographus</I> was abundant on majority of territory – mainly in southern half of area. However locally, it was found in extremely high abundance. The results suggest the need for control measures set up jointly against the most abundant bark beetle species in study region.

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39

Shin. "Influence of Spatial Rainfall Distribution on Sediment Yield: An Experimental Study." Journal of the Korean Society of Civil Engineers 35, no. 1 (2015): 111. http://dx.doi.org/10.12652/ksce.2015.35.1.0111.

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40

Kanevski, Mikhail. "Unsupervised learning of Swiss population spatial distribution." PLOS ONE 16, no. 2 (February 11, 2021): e0246529. http://dx.doi.org/10.1371/journal.pone.0246529.

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The paper deals with the analysis of spatial distribution of Swiss population using fractal concepts and unsupervised learning algorithms. The research methodology is based on the development of a high dimensional feature space by calculating local growth curves, widely used in fractal dimension estimation and on the application of clustering algorithms in order to reveal the patterns of spatial population distribution. The notion “unsupervised” also means, that only some general criteria—density, dimensionality, homogeneity, are used to construct an input feature space, without adding any supervised/expert knowledge. The approach is very powerful and provides a comprehensive local information about density and homogeneity/fractality of spatially distributed point patterns.

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41

Feng, Jing, and Ian Spence. "A Mixture Distribution of Spatial Attention." Experimental Psychology 60, no. 3 (February 1, 2013): 149–56. http://dx.doi.org/10.1027/1618-3169/a000182.

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Although it may seem paradoxical, the unified-focus and multiple-foci theories of spatial selective attention are both well supported by experimental evidence. However, the apparent contradiction is illusory and the two competing views may be reconciled by a closer examination of the spatial mechanisms involved. We propose that the deployment of attention may be modeled as a mixture of individual distributions of attention and we tested this hypothesis in two experiments. Participants had to identify targets among distractors, with the targets presented at various distances from the cued locations. Experiment 1 confirmed that the distribution of attention may be described by a mixture of individual distributions, each centered at a cued location. Experiment 2 showed that cue separation is an important determinant of whether spatial attention is divided or not.

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Baker, William L. "Effect of scale and spatial heterogeneity on fire-interval distributions." Canadian Journal of Forest Research 19, no. 6 (June 1, 1989): 700–706. http://dx.doi.org/10.1139/x89-109.

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The distribution of forest-fire intervals has been characterized by fitting statistical distributions, such as that of Weibull. The parameters of fitted distributions can then be used to compare fire regimes. Fire-interval distributions for the 187-year presettlement fire history record in the Boundary Waters Canoe Area, Minnesota, were analyzed using reconstructed "fire-year" maps. Distributions were determined for sampling units at five spatial scales, from about 25 000 to 400 000 ha. Fire-interval distributions varied from positively to negatively skewed, but for most units the Weibull distribution fit significantly. The distributions varied spatially, and cluster analysis suggested that three fire regions, each containing a relatively homogeneous fire regime, could be identified. The sources of this spatial variation are unknown. There was less variation between scales within a fire region than between fire regions. This contrasts with a previous finding, using the same fire-history data, that scale substantially affects observed landscape age-class distribution. This disparity arises because landscape age-class distributions may fluctuate even if fire-interval distributions do not fluctuate. Reconstruction of fire-interval distributions requires historical data; landscape age-class distributions at an instant in time are insufficient.

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Schaffernicht, Erik, Marco Trincavelli, and Achim J. Lilienthal. "Bayesian Spatial Event Distribution Grid Maps for Modeling the Spatial Distribution of Gas Detection Events." Sensor Letters 12, no. 6 (June 1, 2014): 1142–46. http://dx.doi.org/10.1166/sl.2014.3189.

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Borůvka, L., H. Donátová, and K. Němeček. "Spatial distribution and correlation of soil properties in a field: a case study." Plant, Soil and Environment 48, No. 10 (December 22, 2011): 425–32. http://dx.doi.org/10.17221/4391-pse.

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Analysis of spatial distribution and correlation of soil properties represents an important outset for precision agriculture. This paper presents an analysis of spatial distribution and mutual correlations, both classical and spatial, of soil properties in an agricultural field in Klučov. Clay and fine silt content, pH, organic carbon content (C<sub>org</sub>), moisture (Q), total porosity (Pt), capillary porosity (P<sub>c</sub>), and coefficients of aggregate vulnerability to fast wetting (K<sub>v1</sub>), to slow wetting and drying (K<sub>v2</sub>), and to mechanical impacts (K<sub>v3</sub>) were determined. Semivariogram ranges from 206 m (clay content) to 1120 m (K<sub>v3</sub>) were detected. Many relationships between soil properties were spatially based. Fine silt content and Corg proved to be the most important soil properties controlling all the three aggregate vulnerability coefficients, which was not clear for K<sub>v2</sub> from classical correlation only. Determined spatial correlations and similarities in spatial distribution may serve as groundwork in delineation of different zones for site-specific management.

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Ciannelli, Lorenzo, Valerio Bartolino, and Kung-Sik Chan. "Non-additive and non-stationary properties in the spatial distribution of a large marine fish population." Proceedings of the Royal Society B: Biological Sciences 279, no. 1743 (June 20, 2012): 3635–42. http://dx.doi.org/10.1098/rspb.2012.0849.

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Density-independent and density-dependent variables both affect the spatial distributions of species. However, their effects are often separately addressed using different analytical techniques. We apply a spatially explicit regression framework that incorporates localized, interactive and threshold effects of both density-independent (water temperature) and density-dependent (population abundance) variables, to study the spatial distribution of a well-monitored flatfish population in the eastern Bering Sea. Results indicate that when population biomass was beyond a threshold a further increase in biomass-promoted habitat expansion in a non-additive fashion with water temperature. In contrast, during years of low population size, habitat occupancy was affected positively only by water temperature. These results reveal the spatial signature of intraspecific abundance distribution relationships as well as the non-additive and non-stationary responses of species spatial dynamics. Furthermore, these results underscore the importance of implementing analytical techniques that can simultaneously account for density-dependent and density-independent sources of variability when studying geographical distribution patterns.

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Zhang, Chongliang, Yong Chen, Binduo Xu, Ying Xue, and Yiping Ren. "Evaluating the influence of spatially varying catchability on multispecies distribution modelling." ICES Journal of Marine Science 77, no. 5 (May 9, 2020): 1841–53. http://dx.doi.org/10.1093/icesjms/fsaa068.

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Abstract Varying catchability is a common feature in fisheries and has great impacts on fisheries assessments and species distribution models. However, spatial variations in catchability have been rarely evaluated, especially in the multispecies context. We advocate that the need for multispecies models stands for both challenges and opportunities to handle spatial catchability. This study evaluated the influence of spatially varying catchability on the performance of a novel joint species distribution model, namely Hierarchical Modelling of Species Communities (HMSC). We implemented the model under nine simulation scenarios to account for diverse spatial patterns of catchability and conducted empirical tests using survey data from Yellow Sea, China. Our results showed that ignoring variability in catchability could lead to substantial errors in the inferences of species response to environment. Meanwhile, the models’ predictive power was less impacted, yielding proper predictions of relative abundance. Incorporating a spatially autocorrelated structure substantially improved the predictability of HMSC in both simulation and empirical tests. Nevertheless, combined sources of spatial catchabilities could largely diminish the advantage of HMSC in inference and prediction. We highlight situations where catchability needs to be explicitly accounted for in modelling fish distributions, and suggest directions for future applications and development of JSDMs.

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Chen, Yanqing, Yongsheng Cao, Yanpeng Wu, Lina Chen, and Wei Fang. "Spatial distribution characteristics of crop germplasm resources using spatial statistics." Biodiversity Science 25, no. 11 (2017): 1213–22. http://dx.doi.org/10.17520/biods.2016376.

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INOUE, Kazuya, Ippei MASAKI, Akira KOBAYASHI, and Tsutomu TANAKA. "DISPERSIVITY ESTIMATION USING SPATIAL CONCENTRATION DISTRIBUTION AND SPATIAL MOMENT ANALYSIS." PROCEEDINGS OF HYDRAULIC ENGINEERING 51 (2007): 487–92. http://dx.doi.org/10.2208/prohe.51.487.

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Souris and Demoraes. "Improvement of Spatial Autocorrelation, Kernel Estimation, and Modeling Methods by Spatial Standardization on Distance." ISPRS International Journal of Geo-Information 8, no. 4 (April 24, 2019): 199. http://dx.doi.org/10.3390/ijgi8040199.

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In a point set in dimension superior to 1, the statistical distribution of the number of pairs of points as a function of distance between the points of the pair is not uniform. This distribution is not considered in a large number of classic methods based on spatially weighted means used in spatial analysis, such as spatial autocorrelation indices, kernel interpolation methods, or spatial modeling methods (autoregressive, or geographically weighted). It has a direct impact on the calculations and the results of indices and estimations, and by not taking into account this distribution of the distances, spatial analysis calculations can be biased. In this article, we introduce a "spatial standardization", which corrects and adjusts the calculations with respect to the distribution of point pairs distances. As an example, we apply this correction to the calculation of spatial autocorrelation indices (Moran and Geary indices) and to trend surface calculation (by spatial kernel interpolation) on the results of the 2017 French presidential election.

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Ost, Lieven, Miet van den Eeckhaut, Jean Poesen, and Marie Christine Vanmaercke-Gottigny. "Characteristics and spatial distribution of large landslides in the Flemish Ardennes (Belgium)." Zeitschrift für Geomorphologie 47, no. 3 (September 26, 2003): 329–50. http://dx.doi.org/10.1127/zfg/47/2003/329.

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