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Journal articles on the topic 'Spatial analysis'

To see the other types of publications on this topic, follow the link: Spatial analysis.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Sapaty, P. S. "Holistic spatial analysis of distributed worlds." Mathematical machines and systems 2 (2022): 3–18. http://dx.doi.org/10.34121/1028-9763-2022-2-3-18.

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The paper investigates extended applications of the developed spatial grasp model and technology for analyzing large distributed systems and environments, as well as some examples of solving typical problems in them in the Spatial Grasp Language (SGL). The Spatial Grasp (SG) paradigm allows solving complex problems in a holistic and fully distributed way. It develops in distributed spaces as active ubiquitous waves or even viruses and grasps solutions to spatial problems in parallel pattern-matching mode, fundamentally differing from traditional representations of systems and their solutions as parts that exchange messages. The resultant Spatial Grasp Technology details are briefed where its SGL interpreters can be networked as powerful spatial computers covering any terrestrial and celestial environments and solving problems without any centralized resources. The extended areas for new SG applications include basic environmental issues, global systems, discovering new worlds, Earth science, and planetary exploration activities at NASA. The paper contains descriptions of solutions in SGL to practical problems related to different worlds, including group behavior of marine animals, details of geographical terrain, management of transport networks, and investigation of information networks. The developed paradigm allows direct expression of top semantics and holistic methods for solving complex problems and dynamically composes the needed implementation environments, thus providing the strictest way from problem definition to a practical solution. The formula-like high-level solutions in SGL are extremely compact, often a hundred times shorter than in other languages, and its implementation can be accomplished on any existing platforms, as for the previous language versions in different countries.

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2

Azhari, Septian Cahya, Iman Hilman, Siti Fadjarajani, and Guruh Sukmo. "A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 22, no. 2 (December 8, 2022): 79–94. http://dx.doi.org/10.21009/spatial.222.01.

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This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.

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Azhari, Septian Cahya, Iman Hilman, Siti Fadjarajani, and Guruh Sukmo. "A Bibliometric Analysis: Remote Sensing Literature in Dimensions.ai Database." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 22, no. 2 (December 8, 2022): 91–106. http://dx.doi.org/10.21009/spatial.222.001.

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This research using bibliometric analysis aims to identify and visualize research developments in the field of remote sensing studies. The data is obtained from 2021-2022 through dimensions.ai with criteria for articles that have a digital object identifier (DOI). The data generated were 2645 articles which were then analyzed using the VosViewer device. The data analyzed includes the development of popular keywords, affiliations, countries and authors who do a lot of research in the field of remote sensing. The results of the study found 4 clusters with 216 items for keywords, 6 clusters and 36 items for authors, 15 clusters and 323 items for affiliation, 7 clusters and 61 items for country analysis which mostly conducted research on remote sensing.

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4

Unwin, David J. "GIS, spatial analysis and spatial statistics." Progress in Human Geography 20, no. 4 (December 1996): 540–51. http://dx.doi.org/10.1177/030913259602000408.

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5

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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6

Liu, Yupeng, Wei-Qiang Chen, Tao Lin, and Lijie Gao. "How Spatial Analysis Can Help Enhance Material Stocks and Flows Analysis?" Resources 8, no. 1 (March 4, 2019): 46. http://dx.doi.org/10.3390/resources8010046.

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Spatial information can be integrated into almost all fields of industrial ecology. Many researchers have shown that spatial proximity affects a variety of behaviors and interactions, and thus matters for materials stocks and flows analysis. However, normal tools or models in industrial ecology based on temporal dependence cannot be simply applied to the case of spatial dependence. This paper proposes a framework integrating material stocks and flows analysis with spatial analysis. We argue that spatial analysis can help data management and visualization, determine spatio-temporal patterns-processes-drivers, and finally develop dynamic and spatially explicit models, to improve the performance of simulating and assessing stocks and flows of materials. Scaling in spatial, temporal, and organizational dimensions and other current limitations are also discussed. Combined with spatial analysis, industrial ecology can really be more powerful in achieving its origin and destination—sustainability.

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7

Banerjee, Sudipto. "Spatial Data Analysis." Annual Review of Public Health 37, no. 1 (March 18, 2016): 47–60. http://dx.doi.org/10.1146/annurev-publhealth-032315-021711.

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8

Tobler, Waldo. "Global spatial analysis." Computers, Environment and Urban Systems 26, no. 6 (November 2002): 493–500. http://dx.doi.org/10.1016/s0198-9715(02)00017-0.

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9

Merriam, D. F. "Introductory spatial analysis." Computers & Geosciences 12, no. 6 (January 1986): 819. http://dx.doi.org/10.1016/0098-3004(86)90032-4.

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10

Zhou, Qiming, and Jianfeng Li. "Geo-Spatial Analysis in Hydrology." ISPRS International Journal of Geo-Information 9, no. 7 (July 11, 2020): 435. http://dx.doi.org/10.3390/ijgi9070435.

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With the increasing demand for accurate and reliable hydrological information, geo-spatial analysis plays a more and more important role in hydrological studies. The development of the geo-spatial technique advances our understanding of the complex and spatially heterogeneous hydrological systems. Meanwhile, how to efficiently and effectively process and analyze multi-source geo-spatial data has become more challenging in the fields of hydrology. In this editorial, we first review the development and application of geo-spatial analysis in three major topics in hydrological studies, namely the scaling issue, extraction of basin characteristics, and hydrological modelling. We hence introduce the articles of the Special Issue. These studies present the latest results of geo-spatial analysis in different topics in hydrology, and improve geo-spatial analytic methods for better accuracy and reliability.

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11

Abd Sukor, Nurshuhada, Zubaidi Daud, and Zakri Tarmidi. "Risk Analysis for Sewerage System using Spatial Analysis." Jurnal Kejuruteraan 32, no. 3 (August 30, 2020): 431–40. http://dx.doi.org/10.17576/jkukm-2020-32(3)-08.

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Sewerage systems are important to ensure that wastes discharged at the domestic and industrial levels are well maintained so as not to impact the environment. Sewerage system assets should be monitored and maintained to minimize risks from occurring. Factors that lead to the risk of occurrence of these assets should be identified, as well as location for these risks. This study aims to identify some risks and potential risk locations for the assets in the sewerage system. To achieve this goal, the risk factors that will occur need to be identified. The Skudai north area was chosen as the study area. The analysis is assessing the risk, based on risks factor that was identified, which is the degree of gradient in which the asset resides, as well as the age factor of an aged asset. Higher gradient areas have risks to assets, and in terms of asset age risk, older assets are at higher risk. The analysis results show that most assets are low risk (90.8%), a little of asset at middle risks (8.9%), while high risk assets are not much (0.3%). The results of this analysis can be improved with more factors included to the model. The results of this analysis are expected to assist stakeholders in identifying risky asset positions, thereby assisting in making the best planning and decisions for these risky assets.

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12

Beenstock, Michael, Daniel Felsenstein, and Dai Xieer. "Spatial econometric analysis of spatial general equilibrium." Spatial Economic Analysis 13, no. 3 (December 7, 2017): 356–78. http://dx.doi.org/10.1080/17421772.2018.1403645.

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13

Martin, R. J., and F. Droesbeke. "Spatial Processes and Spatial Time Series Analysis." Journal of the Royal Statistical Society. Series A (Statistics in Society) 151, no. 3 (1988): 553. http://dx.doi.org/10.2307/2983011.

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14

Soleimani moghadam, Hadi, rahman zandi, and ebrahim akbari. "Spatial zoning and spatial analysis of urban poverty using spatial analysis (Case study: Mashhad city)." Journal of Applied researches in Geographical Sciences 21, no. 60 (March 1, 2021): 239–58. http://dx.doi.org/10.52547/jgs.21.60.239.

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15

Sulistyo, Wiwin, Subanar Subanar, and Reza Pulungan. "Development of a Spatial Path-Analysis Method for Spatial Data Analysis." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2456. http://dx.doi.org/10.11591/ijece.v8i4.pp2456-2467.

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Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.

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16

Khalid, Nurul Shakila, Na’asah Nasrudin, and Naimah Osman. "Evaluating Neighbourhood Accessibility: A Spatial Analysis of Walking Preferences." International Journal of Psychosocial Rehabilitation 24, no. 02 (February 12, 2020): 2395–409. http://dx.doi.org/10.37200/ijpr/v24i2/pr200537.

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17

Choi. "Spatial Analysis of Flood Inundation for Ensuring Stream Space." Journal of the Korean Society of Civil Engineers 35, no. 2 (2015): 341. http://dx.doi.org/10.12652/ksce.2015.35.2.0341.

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18

Aditiya, Pandi, Ahmad Herison, Ika Kustiani, and Amril Ma’ruf Siregar. "Investment Analysis of New Port Development Project As a Solution to Overload Problems in Long Ports." Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 19, no. 2 (December 31, 2019): 61–70. http://dx.doi.org/10.21009/spatial.192.7.

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The increasing demand for port services at the Panjang port causes high levels of ship queues. The solution to overcome this problem is to build a new port. However, this new port construction project has not yet conducted a feasibility study. The purpose of this study is to assess the feasibility of port development in the Panjang district of Bandar Lampung in terms of investment or financial aspects. The methods are Benefit-Cost Ratio, Net Present Value, Payback Period, and Internal Rate of Return. The results of the feasibility study of the most profitable investment are in the seventh scenario, the Net Present Value (NPV) of Rp. 463,292,122,605.34, the BCR result of 1.2959, the IRR yield of 12.11% and the Payback Period (PP) occur in the year to thirty-seventh with an economic age of 60 years. The conclusion is that the construction of a new port can be a solution to overcome the overload of Panjang port.

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19

Westheimer, Gerald. "Spatial and spatial-frequency analysis in visual optics." Ophthalmic and Physiological Optics 32, no. 4 (June 14, 2012): 271–81. http://dx.doi.org/10.1111/j.1475-1313.2012.00913.x.

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20

Westheimer, Gerald. "Spatial and spatial-frequency analysis in visual optics." Ophthalmic and Physiological Optics 32, no. 5 (August 13, 2012): 441. http://dx.doi.org/10.1111/j.1475-1313.2012.00938.x.

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21

Johnston, RJ. "W(H)ITHER spatial science and spatial analysis." Futures 29, no. 4-5 (May 1997): 323–36. http://dx.doi.org/10.1016/s0016-3287(97)00014-1.

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22

Sheppard, Eric. "DISSENTING FROM SPATIAL ANALYSIS." Urban Geography 16, no. 4 (May 1995): 283–303. http://dx.doi.org/10.2747/0272-3638.16.4.283.

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23

Tran, Duc Trong. "Spatial analysis in WebGIS." Science and Technology Development Journal 19, no. 2 (June 30, 2016): 75–81. http://dx.doi.org/10.32508/stdj.v19i2.671.

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In most of WebGIS applications, user interactions mainly concentrate on map data editing or data querying. Another aspect with paying less attention is to allow user doing spatial analysis. Spatial analysis can be implemented on server side, by using WPS (Web Processing Service), or on client side, by using JSTS (JavaScript Topology Suite). As an contribution to this aspect, this paper shows how to use to use WPS service of Geoserver and JSTS JavaScript library to carry on spatial analysis activities. A WebGIS system - based on the open source Geoserver and OpenLayers - has been developed and used to perform spatial analysis, searching for spatial features that have spatial relationships with other spatial features. Experiment result shows that the use of WPS and JSTS to perform spatial processing over the internet is practically doable.

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24

Ziegel, Eric R., S. Fotheringham, and Peter Rogerson. "Spatial Analysis and GIS." Technometrics 39, no. 2 (May 1997): 238. http://dx.doi.org/10.2307/1270932.

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25

Palmer, Michael W., Trevor C. Bailey, and Anthony C. Gatrell. "Interactive Spatial Data Analysis." Ecology 77, no. 5 (July 1996): 1642. http://dx.doi.org/10.2307/2265559.

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26

Calder, Alistair. "Spatial analysis ... Why bother?" Statistical Journal of the United Nations Economic Commission for Europe 17, no. 2 (October 1, 2000): 119–23. http://dx.doi.org/10.3233/sju-2000-17203.

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27

Luna, Ronaldo, and J. David Frost. "Spatial Liquefaction Analysis System." Journal of Computing in Civil Engineering 12, no. 1 (January 1998): 48–56. http://dx.doi.org/10.1061/(asce)0887-3801(1998)12:1(48).

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28

Haining, R., S. Wise, and J. Ma. "Exploratory Spatial Data Analysis." Journal of the Royal Statistical Society: Series D (The Statistician) 47, no. 3 (September 1998): 457–69. http://dx.doi.org/10.1111/1467-9884.00147.

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29

Agterberg, Frederik P. "Interactive spatial data analysis." Computers & Geosciences 22, no. 8 (October 1996): 953–54. http://dx.doi.org/10.1016/s0098-3004(96)80468-7.

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30

Graham, A. J., P. M. Atkinson, and F. M. Danson. "Spatial analysis for epidemiology." Acta Tropica 91, no. 3 (August 2004): 219–25. http://dx.doi.org/10.1016/j.actatropica.2004.05.001.

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31

Nijkamp, Peter. "Discrete spatial choice analysis." Regional Science and Urban Economics 17, no. 1 (February 1987): 1–2. http://dx.doi.org/10.1016/0166-0462(87)90065-2.

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32

Lopes, Hedibert Freitas, Esther Salazar, and Dani Gamerman. "Spatial dynamic factor analysis." Bayesian Analysis 3, no. 4 (December 2008): 759–92. http://dx.doi.org/10.1214/08-ba329.

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33

Haslett, John. "Spatial Data Analysis-Challenges." Statistician 41, no. 3 (1992): 271. http://dx.doi.org/10.2307/2348549.

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34

Tremlett, Paul-François. "Religion and spatial analysis." Material Religion 9, no. 1 (March 2013): 116–17. http://dx.doi.org/10.2752/175183413x13535214684177.

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35

Snickars, F. "Formal spatial economic analysis." European Journal of Operational Research 19, no. 3 (March 1985): 403–5. http://dx.doi.org/10.1016/0377-2217(85)90143-2.

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36

Clark, Chris D., and Colin Wilson. "Spatial analysis of lineaments." Computers & Geosciences 20, no. 7-8 (August 1994): 1237–58. http://dx.doi.org/10.1016/0098-3004(94)90073-6.

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37

Finkelshtein, Dmitri, Yuri Kondratiev, Eugene Lytvynov, and Maria João Oliveira. "Stirling operators in spatial combinatorics." Journal of Functional Analysis 282, no. 2 (January 2022): 109285. http://dx.doi.org/10.1016/j.jfa.2021.109285.

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38

Shi, Junping, Chuncheng Wang, Hao Wang, and Xiangping Yan. "Diffusive Spatial Movement with Memory." Journal of Dynamics and Differential Equations 32, no. 2 (May 6, 2019): 979–1002. http://dx.doi.org/10.1007/s10884-019-09757-y.

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39

Samadi, Dr. "PENGARUH PERILAKU BERTANGGUNG JAWAB DAN PEMAHAMAN SISWA SMA DI DKI JAKARTA TERHADAP KETAHANAN SOSIAL EKOLOGIS LOKAL." Jurnal SPATIAL Wahana Komunikasi dan Informasi Geografi 18, no. 1 (July 5, 2018): 72–76. http://dx.doi.org/10.21009/spatial.181.04.

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This study aims to determine empirically the influence of responsible behavior toward ecological social resilience in the framework of environmental education in senior high school students. The research was conducted during August-December 2017. This research used experimental method with 2 X 2 factorial design. The population in this study is the affordable population with random sampling method. The test of statistical hypothesis used ANAVA (analysis of variance) by comparing the Fcount with Ftable on each treatment factor (A and B), and interaction between factors (Ax B). Data analysis in this study using Pearson correlation analysis and regression analysis where the normality test using Kolmogorov-Smirnov method. The result of the research concludes that the stronger the responsible behavior toward the environment, the more its ecological socio-economic resilience in the framework of applying of environmental education to high school students in DKI Jakarta. Keyword: Rresponsible behavior, Ecological social resilience, Environmental education in senior high school, Jakarta.

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40

Ospina, Oscar, Roberto Manjarres-Betancur, Guillermo Gonzalez-Calderon, Alex C. Soupir, Inna Smalley, Kenneth Tsai, Joseph Markowitz, Anders Berglund, Xiaoqing Yu, and Brooke L. Fridley. "Abstract 876: spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics." Cancer Research 84, no. 6_Supplement (March 22, 2024): 876. http://dx.doi.org/10.1158/1538-7445.am2024-876.

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Abstract The use of single-cell RNA-seq (scRNA-seq) to understand the complexity of the tumor microenvironment (TME) has been pivotal in advancing cancer research and treatment strategies. Nevertheless, locating the individual TME components in their spatial contexts is essential to understanding the complexity of the TME, tumorigenesis, metastasis, and drug response. The recent popularity of spatially resolved transcriptomics (SRT) allows researchers to describe the TME and their spatial neighborhoods; however, SRT data analysis can be challenging for non-data scientists. Thus, we have developed spatialGE, a point-and-click web application designed to empower researchers in the analysis of SRT data. Our application offers a suite of functionalities, including the visualization of spatial gene expression activity maps, quantification of the spatial heterogeneity, testing of pathway-level spatial aggregation, tissue domain/niche detection, detection of spatial gradients in expression patterns, and differential expression analysis. Furthermore, spatialGE allows direct comparative analysis of gene expression with the morphology in tissue images, as well as methods for cell/domain phenotyping. Data input has been streamlined and made flexible to receive outputs from multiple SRT platforms. Analyses and results are stored within user-defined projects to facilitate accessibility at any time. We demonstrate the usefulness of spatialGE through its application to leptomeningeal metastatic melanoma (LMM) tissues assayed with the Visium platform. The gene expression tissue domain detection and gradient analysis in spatialGE indicated that SERPINA3 expression is higher in tumor regions closer to stroma. This finding led to cell culture experiments indicating that knocking down SERPINA3 results in the sensitization of tumors to MAPK-targeting therapy. These results highlight the utility of spatialGE for exploring SRT data and the empowerment of cancer researchers to explore the spatial architecture of the TME. Citation Format: Oscar Ospina, Roberto Manjarres-Betancur, Guillermo Gonzalez-Calderon, Alex C. Soupir, Inna Smalley, Kenneth Tsai, Joseph Markowitz, Anders Berglund, Xiaoqing Yu, Brooke L. Fridley. spatialGE: Empowering researchers to study the tumor microenvironment leveraging spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 876.

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41

Dr. S. S. Motebennur, Dr S. S. Motebennur, and Sri Siddarudha Nilaya. "Spatial Analysis of Population Structure in Dharwad District Karnataka State." Indian Journal of Applied Research 3, no. 8 (October 1, 2011): 324–27. http://dx.doi.org/10.15373/2249555x/aug2013/101.

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42

Dondu, Mustafa, Nedim OzdemiR, Mesut Perktash, and Elmina Gadirova. "SPATIAL ANALYSIS AND EVALUATION OF WATER QUALITY OF GOKOVA BAY." JOURNAL OF BIOLOGY AND ECOLOGY 2, no. 2 (February 28, 2020): 51–55. http://dx.doi.org/10.26739/2181-0575-2020-2-9.

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43

Erdoğan, Gizem. "IDENTIFIYING URBAN FRINGE SPATIAL CHARACTER WITH FRACTAL ANALYSIS: AKHAN, DENİZLİ." E-journal of New World Sciences Academy 14, no. 2 (April 29, 2019): 11–17. http://dx.doi.org/10.12739/nwsa.2019.14.2.e0045.

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44

Bodunde, Olaiya. "Spatial Analysis of Accident Prone Areas of Ondo State, Nigeria." International Journal of Research Publication and Reviews 4, no. 12 (December 2, 2023): 185–95. http://dx.doi.org/10.55248/gengpi.4.1223.123306.

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45

Tarmidi, Zakri, Wei Kang Yap, Hua Xian Kon, Suzanna Azmy, and Noordyana Hassan. "Analysing Spatial Pattern of the Three Major Fast-Food Chain Restaurants in Johor Bahru Johor, Malaysia." Journal of Advanced Geospatial Science & Technology 3, no. 2 (August 30, 2023): 63–76. http://dx.doi.org/10.11113/jagst.v3n2.64.

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Spatial analysis has proven to be an impactful tool in business for gaining insights and making informed decisions, especially in analysing spatial patterns and finding new locations for starting or expanding the business. However, most fast-food chain restaurants have t yet to optimise the use of GIS, especially in analysing the spatial distribution patterns of their outlets. This study aims to analyse the spatial distribution patterns of three major fast-food chains in Johor Bahru, Malaysia: KFC, McDonald’s, and Burger King. Four spatial analyses were performed: network analysis, buffer analysis, heatmap analysis, and spatial distribution pattern analysis using the nearest neighbour technique. The results showed different spatial distribution patterns for these three major fast-food chains. KFC is more spatially randomly distributed, concentrated in downtown Johor Bahru, while McDonald’s is spatially scattered throughout the Johor Bahru district and the Skudai area. Burger King has too few outlets and is spatially scattered in the Johor Bahru district. From these results, fast-food franchises can use the information to locate new locations based on their indicators for new outlets.

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46

Dučinskas, K., and J. Šaltytė. "Quadratic Discriminant Analysis of Spatially Correlated Data." Nonlinear Analysis: Modelling and Control 6, no. 2 (December 5, 2001): 15–28. http://dx.doi.org/10.15388/na.2001.6.1.15212.

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The problem of classification of the realisation of the stationary univariate Gaussian random field into one of two populations with different means and different factorised covariance matrices is considered. In such a case optimal classification rule in the sense of minimum probability of misclassification is associated with non-linear (quadratic) discriminant function. Unknown means and the covariance matrices of the feature vector components are estimated from spatially correlated training samples using the maximum likelihood approach and assuming spatial correlations to be known. Explicit formula of Bayes error rate and the first-order asymptotic expansion of the expected error rate associated with quadratic plug-in discriminant function are presented. A set of numerical calculations for the spherical spatial correlation function is performed and two different spatial sampling designs are compared.

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47

Ariyanti, Oktavia, Cahyadi Setiawan, and Fauzi Ramadhoan A'Rachman. "Evaluasi Kesesuaian Lahan Tanaman Padi Sawah di Desa Weninggalih, Kecamatan Jonggol, Kabupaten Bogor." Jurnal Spatial Wahana Komunikasi dan Informasi Geografi 22, no. 1 (March 31, 2022): 1–12. http://dx.doi.org/10.21009/spatial.221.3.

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The need for rice is certainly increasing along with the increase in the population of a region. Weninggalih is one of the villages in Bogor Regency where most of the land cover (63%) is rice fields. The land can only be harvested once a year. So that the area of rice fields is not proportional to the results of rice production produced. These problems raise the interest of researchers in conducting land evaluation analysis on lowland rice plants. The data used comes from the results of data collection with observation techniques and literature study. The sample used was 35 units based on the matching analysis technique to produce an evaluation analysis of lowland rice plants. The matching results show that there are two land suitability classes including marginally suitable (S3) and not suitable (N). As much as 254.20 ha (82.17%) is a marginal suitability class (S3), while 55.20 ha (17.82%) is not suitable (N). The results of the analysis are limited by several factors, including water availability, drainage, soil CEC, base saturation, soil pH, organic C, total N, P2O5, K2O, slope, erosion hazard, inundation height, duration of inundation, and rocks on the surface. After making improvements to the actual land, 309.72 ha became a potential class with a very suitable land suitability class (S1).

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48

Yao, Xiaobai, and Shanqi Zhang. "Social-spatial structure of Beijing: a spatial-temporal analysis." International Journal of Society Systems Science 6, no. 1 (2014): 18. http://dx.doi.org/10.1504/ijsss.2014.059923.

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Lombard, P., M. Zaidi, S. Mansouri, G. Zadeh, and B. Wouters. "Spatial transcriptomics analysis for spatial biomarker discovery in glioblastoma." European Journal of Cancer 174 (October 2022): S121—S122. http://dx.doi.org/10.1016/s0959-8049(22)01123-6.

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Fernández-Avilés Calderón, Gema. "Spatial Regression Analysis vs. Kriging Methods for Spatial Estimation." International Advances in Economic Research 15, no. 1 (September 24, 2008): 44–58. http://dx.doi.org/10.1007/s11294-008-9189-0.

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