nach oben

Earth Science Informatics

Erschienen in:

06.09.2023 | Research

Multidimensional spatial autocorrelation analysis and it’s application based on improved Moran’s I

verfasst von: Ce Zhang, Wangyong Lv, Ping Zhang, Jiacheng Song

Erschienen in: Earth Science Informatics | Ausgabe 4/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper aims to improve and extend the improved spatial Moran’s I theory by analyzing multi-observation samples. By constructing an expanded spatial weight matrix, a vector definition of the improved spatial Moran’s I is given. In order to improve the judgment basis of the improved spatial Moran’s I, the range of the improved spatial Moran’s I is derived using the non-negativity of variance. Since the improved Moran’s I is only applicable to the analysis of a single variable with unknown distribution, a Moran’s I matrix suitable for analyzing the spatial autocorrelation of multiple variables is proposed. The distribution of the elements of the Moran’s I matrix is studied by Monte Carlo simulation. The simulation results show that only the elements on the non-main diagonal follow a normal distribution when the sample size is small. Any element follows a normal distribution when the sample size is large. Then it is proved that the Moran’s I matrix follows a Wishart distribution when the spatial weight matrix is a positive definite matrix. Finally, several comprehensive evaluation indicators suitable for the theory of multivariate spatial autocorrelation are proposed based on the algebraic meaning of the Moran’s I matrix. Spatial autocorrelation analysis is carried out in combination with multi-dimensional air pollution data.

Vorheriger Artikel Analyzing groundwater level with hybrid ANN and ANFIS using metaheuristic optimization

Nächster Artikel An autoencoder-based stacked LSTM transfer learning model for EC forecasting

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Moran PA (1948) The interpretation of statistical maps. J R Stat Soc. Series B 10(2):243–251

Geary RC (1954) The contiguity ratio and statistical mapping. The incorporated statistician 5(3):115–146CrossRef

Sokal RR, Oden NL (1978) Spatial autocorrelation in biology: 1. methodology. Biol J Linn Soc 10(2):199–228CrossRef

Jiao L, Liu Y (2012) Analyzing the spatial autocorrelation of regional urban datum land price. Geo-spatial Information Science 15(4):263–269CrossRef

Bone C, Wulder MA, White JC (2013) A gis-based risk rating of forest insect outbreaks using aerial overview surveys and the local moran’s i statistic. Appl Geogr 40:161–170CrossRef

Liang J, Feng C, Zeng G (2017) Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, lianyuan, china. Environ Pollut 225(17):681–690CrossRef

Zhang Y (2020) Analysis of the spatial effects of inter-provincial air pollution in china. E3S Web of Conferences 194(1):1–4

Moran PA (1950) Notes on continuous stochastic phenomena. Biometrika 37(1/2):17–23CrossRef

Getis A, Ord JK (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24(3):189–206CrossRef

Ord JK, Getis A (1995) Local spatial autocorrelation statistics: Distributional issues and an application. Geogr Anal 27:286–306CrossRef

Anselin L (1995) Local indicators of spatial association-lisa. Geogr Anal 27(2):93–115CrossRef

Assunccedil RM, Reis EA (1999) A new proposal to adjust moran’s i for population density. Stat Med 18:2147–2162CrossRef

Fotheringham AS, Brunsdon C, Charlton M (2003) Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons

Getis A (1995) Cliff, ad and ord, jk 1973: Spatial autocorrelation. london Pion. Prog Hum Geogr 19(2):245–249CrossRef

Bivand D, Wong RS (2018) Comparing implementations of global and local indicators of spatial association. Test 27:716–748CrossRef

Ren T, Long Z, Zhang R, Chen Q (2014) Moran’s i test of spatial panel data model - based on bootstrap method. Economic Modelling 41:9–14CrossRef

Ou B, Zhao X, Wang M (2015) Power of moran’s i test for spatial dependence in panel data models with time varying spatial weights matrices. Journal of Systems Science and Information 3:1–10CrossRef

Carrijo TB, da Silva AR (2017) Modified moran’s i for small samples. Geogr Anal 49(4):451–467CrossRef

Gan M, Lv W, Fu L (2016) Spatial statistical analysis of pm2.5 in chengdu based on the improved moran’s i index. Environ Sci Technol 39(9):187–193

Wang Y, Lv W, Wang M, Chen X, Li Y (2023) Application of improved moran’s i in the evaluation of urban spatial development. Spatial Statistics 33(5):82–101

Tiefelsdorf M, Boots B (1995) The exact distribution of moran’s i. Environment and Planning A 27(6):985–999CrossRef

Diawara N, Waller L, King R, Lorio J (2019) Simulations of local moran’s index in a spatio-temporal setting. Comm. Statist. Simulation Comput 48:1849–1859CrossRef

Lv Y (2018) Robust estimation of spatial autoregressive models, Master’s thesis. Yunnan University of Finance and Economics

Jiang L (2016) Reflections on the choice of spatial regression models. Statistics and Information Forum 31(10):10–16

Zhu H, Liu S, Jia S et al (2004) Several issues in the spatial interpolation of natural geographical elements. Geographical studies 23(4):425–432

He H, Guo Z, Xiao W (2005) Research progress in spatial interpolation of precipitation. J Ecol 24(10):1187–1191

Zhao Y, Liu X, Sun T (2013) Prediction of arable land area change in china based on a spatial autoregressive model. Arid Zone Resources and Environment 27(8):1–5

Levine N (2006) Crime mapping and the crimestat program. Geogr Anal 38(1):41–56CrossRef

Xue B, Xiao X, Li J (2020) Identification method and empirical study of urban industrial spatial relationship based on poi big data: a case of shenyang city, china. Geography and Sustainability 1(2):152–162CrossRef

Ding W, Gao D, Luo H et al (2020) Study on the spatial variation of human development levels in countries or regions along the belt and road. Resource Development and Markets 36(11):1219–1226

Barbosa CC, do Bonfim CV, de Brito CMG, et al (2018) Spatial analysis of reported new cases and local risk of leprosy in hyper-endemic situation in northeastern brazil. Trop Med Int Health 23(7):748–757

Wang W, Liu SN, Yin P, Wang L et al (2021) Impact of different spatial weight matrices on spatial autocorrelation analysis of cardiovascular disease deaths in china. Chinese Journal of Epidemiology 42(8):1437–1444

Tao J, Fu W, Jiang P et al (2014) Spatial distribution of soil organic carbon in zhejiang forests based on moran’s i and geostatistics. Journal of Nanjing Forestry University 57(5):97–101

Jin F, Lee L (2015) On the bootstrap for moran’s i test for spatial dependence. J Econ 184(2):295–314

Xin T, Yu H (2021) Spatial distribution and influencing factors of obesity rates among top chinese university students: 543. Med Sci Sports Exerc 53(8):182–197CrossRef

Ha H, Tu W (2018) An ecological study on the spatially varying relationship between county-level suicide rates and altitude in the united states. International journal of environmental research and public health 15(4):671–686CrossRef

Shen T, Li F, Chen Z (2022) Evaluation of urban vitality and spatial correlation analysis based on multi-source data - taking changzhou city’s main urban area as an example. Yangtze River Basin Resources and Environment 31(5):1006–1015

Feng J, Chen T, Dai L et al (2022) Bivariate spatial autocorrelation analysis of influenza vaccination rates and socio-economic indicators among children aged 6 months-5 years during the influenza season 2020–2021 in guizhou province. Vaccines and Immunization in China 28(2):199–203

Shao W (2012) Monte carlo methods and their application to some statistical models. Ph.D. thesis, Jinan: Shandong University

Luo Z (2020) Analysis of factors influencing house prices in major cities in china based on a spatial panel model, Master’s thesis, Southwest University of Finance and Economics

Yang S, Fan BK, Gu Y (2022) Non-linear effects of investment-based environmental regulation on green total factor productivity. China Population - Resources and Environment 32(5):120–131

Wang P, Zeng C, Song Y et al (2021) The spatial effect of administrative division on land-use intensity. Land 10(5):1–18CrossRef

Ouimet F (2022) A symmetric matrix-variate normal local approximation for the wishart distribution and some applications. J Multivar Anal 189(1):1–17

Caro-Lopera FJ, Farías GG, Balakrishnan N (2022) Matrix variate distribution theory under elliptical models-v: The non-central wishart and inverted wishart distributions. Mathematical Methods of Statistics 31(1):18–42CrossRef

Jaya IGNM, Andriyana Y, Tantular B et al (2019) Spatiotemporal dengue disease clustering by means local spatiotemporal moran’s index. IOP Conference Series: Materials Science and Engineering 621(1):1–11CrossRef

Fang C, Liu H, Li G et al (2015) Estimating the impact of urbanization on air quality in china using spatial regression models. Sustainability 7(11):15570–15592CrossRef

Titel: Multidimensional spatial autocorrelation analysis and it’s application based on improved Moran’s I
verfasst von: Ce Zhang
Wangyong Lv
Ping Zhang
Jiacheng Song
Publikationsdatum: 06.09.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 4/2023
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-023-01090-9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2023

Improving permeability prediction in carbonate reservoirs through gradient boosting hyperparameter tuning

Integrating watershed segmentation algorithm and supervised Bayesian classification for the assessment of petrophysical parameters, pore properties, and lithofacies: a case study from Abadan Plain, SW Iran

EQAFR: an energy and QoS aware fuzzy routing for internet of underwater things using SDN

A new approach to mechanical brittleness index modeling based on conventional well logs using hybrid algorithms

Spectral-spatial dynamic graph convolutional network for hyperspectral image classification

Application of a hybrid deep learning approach with attention mechanism for evapotranspiration prediction: a case study from the Mount Tai region, China

Premium Partner