Comments on: Spatial Autocorrelation and Moran’s I in GIS

By: Isaac Ajao

Isaac Ajao — Sun, 19 Mar 2023 20:28:44 +0000

I don’t think you need spatial dependence analysis on this. Just carry out a survey. Ask questions using questionnaires or interview where they live, how close they are to railroad. Administer them to the people close to railway, ask of health challenges they have. Cancer should be one of the options to pick

By: Mili

Mili — Mon, 13 Jun 2022 18:06:31 +0000

I want to know that can I apply spatial auto correlation Moran’s I in identifying the location patterns of schools in a region. Whether the location patterns of the schools are clustered, dispersed, or random.

By: Iman

Iman — Fri, 16 Jun 2017 10:43:21 +0000

Thank you for these good info

I have three different configurations of points in my study area. Also I have 5 different variables (altitude, slope, …). My purpose is to select one of these point sets with least autocorrelation in the modeling process.

My question is that I must calculate Moran’s I for all of variables (altitude, slope, …) separately before running the model or I must run the model and calculate Moran’s I for the output layer?

By: Stephanie

Stephanie — Wed, 22 Mar 2017 18:04:11 +0000

Hi, I have one question. I am trying to determine spatial autocorrelation of values of a continuous raster. I’m using ArcGIS and cannot use Morans I on a raster, so I’ve converted my raster to points. Am I correct in doing so or is there a better way to compute autocorrelation of values in a raster?

By: Ross

Ross — Tue, 08 Nov 2016 23:07:58 +0000

Mitra: you don’t have to worry about spatial autocorrelation before running a kernel density estimation (KDE).

By: mitra

mitra — Mon, 08 Aug 2016 15:51:14 +0000

Hi,
I have an important question, Is spatial auto-correlation necessary before using kernel density estimation in GIS on my accident data? or two method are separate from each other and I can use kernel density without any limitation?
thank you

By: Vitalis

Vitalis — Thu, 28 Apr 2016 23:13:30 +0000

Which metric is the most appropriate to measure the spatial autocorrelation of a discrete point dataset? Moran’s I has been shown to work well with continuous data, and therefore cannot be used, where as the joint count statistics works well with discrete data (e.g, binary presence/absence matrix) representing an area and not point data. Any suggestions will be appreciated.

By: Aileen

Aileen — Tue, 26 Apr 2016 20:03:33 +0000

Take a look at he coefficient of areal correlation.

By: GISGeography

GISGeography — Sun, 06 Dec 2015 12:39:29 +0000

You’d be more interested in the exploratory regression and regression analysis in ArcGIS. Proximity to roads is a buffer, and other layers are predictor (independent) variables. While cancer cases (yes/no) are the dependent variable. This helps find relationships between layers

By: Jesse Taylor

Jesse Taylor — Sun, 06 Dec 2015 09:24:20 +0000

Excellent article – thank you. I was wondering if there are related statistical concepts that measure correlation between objects in *different* layers. For instance, what if I wanted to see if there was some sort of spatial correlation between the locations of railroad tracks and cancer rates? (i.e. does proximity to railroad tracks correlate with increased cancer rates?) Which functions can help find relationships of this sort?