# statistics for spatial data cressie

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### Vernacular spatialstatistics: spatial autocorrelation

".The study of spatial autocorrelation is a very important concept to reveal the spatial data distribution, and the calculation of the degree of correlation in spatial autocorrelation is the main method to study spatial autocorrelation.So, in the next issue, let's talk about

### Vernacular SpatialStatistics 24: Geographic weighted regression (iii)

This chapter has a mathematical formula ... Beware of those who are allergic to maths ... The previous article continued, the book connected to a back ... Last time, in the improvement of global regression on the basis of GWR finally turned out, from the Space analysis field finally has its own dedicated regression algorithm. If the spatial statistics are different from the two major characteristics of cla

### Vernacular SpatialStatistics 24: Geographic weighted regression (iv)

Originally this chapter is ready to write (copy) the ArcGIS Help document, write the use of the geo-weighted regression tool ..., and then directly end the geographical weighted regression, but recently received a lot of students e-mail, many of them were dropped in the shrimp God dug out of the big pit inside, such as write a method, not listed formula, Another example of writing a formula wood has a derivation process (...). As a shrimp God of high numbers, he knew me and I did not know him. ）

### Vernacular Space Statistics 15: Multi-distance spatial cluster analysis (Ripley's K function) (upper)

The two data, they show the confidence interval information for each iteration calculation (specified by the distance segment quantity parameter). if When the observed K value is greater than the hiconfenv value, the spatial clustering of the distance is statistically significant. If the observed K value is less than the lwconfenv value, the spatial dispersion o

### Vernacular spatialStatistics Four: P-values and Z-VALUES (top): 0 hypothesis

outliers.(Another way of thinking about tossing coins is to read this article: Two ways of thinking about coin toss:http://mp.weixin.qq.com/s?__biz=MzA4ODk4NzgyNA==mid=200720156idx=1sn= 564f0b6fe95276180c625373a7cea70f#rdIn the classic statistics, 0 assumes that you calculate the data is in line with a certain probability, then in the spatial

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### Vernacular spatialStatistics 14: Clustering of high/low values (upper)

From the last 0 hypothesis, we all know that we have to go into all sorts of magical statistical theory stages, but because of the Wu Dao, I try not to write the official flavor of the white paper.today we're going to talk about an advanced measure of spatial autocorrelation: High / clustering of low values. Previously, the relationship between spatial data is no

### Six of vernacular spatialstatistics: the average nearest neighbor

In the previous article, we looked at a lot of the basic principles of distance and clustering, starting with this chapter, we talked about some specific tools and algorithms.Before we use the Moran index, p-value, Z-score What, we can get a copy of the data is discrete, random or aggregation, if more than one data is aggregated, which of the data is the highest

### SpatialData Analysis and R language practices

grid data; then, it introduces spatial point pattern analysis, interpolation and geographic statistics analysis, surface data, spatial self-correlation analysis, and surface data modeling. Finally, it introduces the application o

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