The field of study concerning statistical methods that use space and spatial relationships (such as distance, area, volume, length, height, orientation, centrality and/or other spatial characteristics of data) directly in their mathematical computations.

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3
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1answer
37 views

Spatial autoregressive model implementation in R

I need to implement a SAR model with no covariates. To be more specific, the regression I have to estimate is y=bWy+e where: y is the dependent variable; b is the coefficient parameter to be ...
0
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0answers
22 views

What does Boston housing data be projected? [on hold]

Boston housing data is very famous spatial data. When, however, we would like to visualize the data mapping, we always are suffered from lacking projection file (.prj) in the files providing GeoDa ...
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0answers
13 views

create_WX() always creates NA [migrated]

Using spdep for R (a collection of tools for spatial econometrics), I wanted to build a regression matrix myself. The matrix is the combination of two matrices, one ...
0
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0answers
9 views

How to use categorical explanatory variables within cokriging models [on hold]

I am using cokriging to model a continuous variable across my study unit (soil carbon). I have one continuous explanatory covariable (soil moisture) but also a categorical explanatory covariable ...
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0answers
5 views

Is it possible to extend dynamic spatial (space-time) panel model to a SARAR (SAC) specification?

I have panel data of 200 regions over 20 years. My goal is to estimate a dynamic spatial (space-time) panel model. I would like to employ an extension of model used in Debarsy/Ertur/LeSage (2009): ...
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3answers
53 views

Any easy way to cluster GPS trajectories?

Can anyone recommend an easy way to cluster hundreds of GPS trajectories to find out their common paths? The GPS data is coming from different vehicles that have traveled thousands of miles.
3
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0answers
19 views

Test whether cross-sectional dependence in panel data follows known (network/spatial) structure

I want to test whether cross-sectional dependence in one specific variable (y) in panel data format follows a known structure (W) (e.g. network, spatial dependence), after controlling for individual ...
2
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1answer
19 views

Estimating a peer effects model in R

I would like to estimate a model of the following form: $$ y = \sigma G y + \beta X + \delta G^* X + \epsilon $$ where $G$ and $G^*$ are quadratic adjacency matrices, $y$ is a vector of a dependent ...
0
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1answer
17 views

Homogeneous vs. Inhomogeneous Poisson point process

What are the main theorical differences between the homogeneous and inhomogeneous Poisson point process? What are the aspects and condition of my data that I can determine which point process best ...
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0answers
7 views

Spatial regression-one explanatory variable is determined by other explanatory variables

I am doing a spatial analysis of the relationship between nutrient loss, intensive inputs and farm characteristics. However, the dairy yield is also determined by intensive inputs; also, dairy yield ...
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0answers
19 views

How do I interpret lagsarlm output from R's spdep?

I've run lagsarlm on my dataset, using a mixed model and using a row-standardized adjacency matrix. I have results that I think are good, but would am not sure how ...
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0answers
6 views

Use of mantel test to test spatial correlation between single and mixed-species groups

I am trying to see if there is spatial correlation between species found in single and mixed-species sightings using location-specific data. I used a mantel test (geographic distance, Euclidean) in ...
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0answers
9 views

Calculating leverage/cook's distance for a Weighted Spatial Simultaneous Autoregression Model

I am estimating a Weighted Spatial Simultaneous Autoregression Model (spdep::spautolm --> Link) in R and I would like to do some residual analysis. Unfortunately ...
0
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1answer
26 views

Computing Issues with Kriging

I am having some issues with Kriging in R, and I was looking for some idea where I am going wrong. From what I can tell, I done a decent job removing the trend, and I believe my transformed data is ...
0
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0answers
22 views

How can I compute multicollinearity (VIF) in R, and know if it's safe?

I am working on a group project for a university course on "big data research methods". My data is aggregated by neighborhoods in Chicago. My dependent variable $Y$ is the property crime rate (per ...
0
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0answers
23 views

Lag lengths are larger than my domain in gstat in R? variogram object

My problem is that the my resulting variograms are of a larger lag length than my domain. I have the following code to compute lags in the vertical direction: ...
0
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0answers
22 views

How to compare Pearson correlation coefficients from spatial data [duplicate]

Dear "Cross Validated" community, First I want to introduce my data to you, so that you can get a clear overview regarding the problem I´m confronted with. For some urban studies I´ve got a spatial ...
2
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0answers
12 views

Correlating aggregated values between groups

I am currently analysing data from household surveys in 27 Indian villages in 7 states, with about 30 cases in each village (807 in total). The aim is to find relationships between various factors and ...
0
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1answer
15 views

Is spatial eigenfunction analysis a kind of direct gradient analysis?

Everything is in the title. By eigenfunction analysis, I mean using methods based on Moran's and asymmetrical eigenvector maps. I have a trouble to understand if they overlap or if one is just a ...
1
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0answers
25 views

Help with R packages to use in my spatial discrete choice model

I am at a loss as to which R package and approach to use for my modeling. The background is: I have a transportation cross-sectional survey dataset combined with many other GIS datasets. I am ...
1
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1answer
40 views

Condensing spatial time series data and spatial interpolation

I have spatio-temporal albedo (roughly, the 'reflectivity' of earth's surface) dataset, from NASA's MODIS satellite, for a 130 square kilometer area. The dataset contains raster files in the NetCDF ...
2
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0answers
43 views

Is there any difference between spatial correlation and spatial autocorrelation?

What is spatial correlation? Is it the same with spatial auto-correlation or it is different?
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0answers
24 views

Is it meaningful to compute a radial kernel density estimate from 2D data?

I am working with 2D spatial data, $(X_i, Y_i),\; i=1, \cdots, N$. My current research requires estimating the density of the distances between those data points in each of the two dimensions. So ...
2
votes
1answer
36 views

MAUP: correlation increases with aggregation, would error decrease?

I know that, due to MAUP, as you coarsen the scale of analysis, you can expect the correlation coefficient to increase, variance to decrease (?), and regression coefficients to fluctuate. Is there ...
3
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0answers
47 views

What is the theory behind using eigenvectors for spatial filtering

As a programmer I have used the spdep package successfully for spatial filtering. But would appreciate it if someone could offer a description (preferably with supporting references) of how this ...
1
vote
1answer
106 views

Fitting a variogram model with the pairwise distance matrix supplied

I'm trying to fit a variogram to my data, however the spatial points are confined by an irregular polygon. So I'd like to supply a variogram model function with the distance matrix of the points. ...
0
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0answers
26 views

Geostatistics: disaggregating regional data to subregions

I have been reading a lot on geostatistics. I am still unsure this problem falls under the umbrella of geostatistics, or if it is statistically feasible at all. I have the market share for a company ...
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0answers
30 views

Incorporating systematic error in (spatial) predictive modelling

I have created a model (random forest) and withheld 20%. When I apply the model to the withheld dataset and check the residuals against the real values I can see there is a systematic error e.g lower ...
0
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1answer
36 views

Mantel test and Moran I giving different result for same data sample

I want to check spatial correlation for my data about fecal coliform values in water measured at different location. For this I have tried both Moran.I and the Mantel test. They are giving different ...
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0answers
33 views

Does negative binomial regression assume sample independence?

I'm working with a negative binomial multiple regression and I'm wondering about the assumption of spatial independence of samples. White and Bennetts (1996) say that the assumption of spatial ...
2
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0answers
35 views

Interpretation of scaled data in a logistic regression analysis

I have a variable (distance to road; measurement=meters; see below) that contains a right-skew and percentage data (0-100% based on amount of habitat in a moving window; see example below) in a ...
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0answers
89 views

Manual two-way demeaning panel data in R

I'm trying to manually demean panel data by both time demeaning and cross-sectional demeaning, yet haven't been able to do it well. The best thing I can think of is looping through the means for each ...
2
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0answers
11 views

How do I demonstrate threshold proximity between the movement of one object on another?

I'm hoping you can advise on what may be a very simple problem. Here's the scenario: I have several maps of an estuary, each separated by ten years from the last. On each map are two variables: ...
4
votes
1answer
243 views

Feasibility of Negative Binomial Spatial Regression

I have a set of crime count data where it appears that the data take on a negative binomial distribution. I have had some success converting the dependent variable (a crime count) into a rate and then ...
0
votes
1answer
39 views

What to make of countervailing spatial regression coefficients?

I am running regressions across a country's counties (N about 300). I divide the country in two regions A and B to control for potential unobservables. My explanatory variable varies at the county ...
2
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0answers
50 views

how to bootstrap data on irregular grid?

I have irregular gridded data and I wish to resample from this data in such a way that the spatial correlation in the data is preserved. I will assume that grids that are within 300Kms of one another ...
0
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1answer
19 views

Learning a spatial function

I have some observations of a variable y, that varies spatially. For each observation, I also have a lat, long tuple. I have some 50 or so observations. Besides conducting some exploratory analysis ...
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0answers
31 views

Testing whether neighbors are more similar to each other than distant points

Suppose you have points x1...xn in a metric space X, each is associated with some measurement y1...yn. I want to test whether points closer to each other in X have similar associated value y. I have ...
3
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0answers
44 views

Difference between spatially autocorrelated logit methods

I am seeking advice on different methods to account for spatial autocorrelation in logit models. I've seen a lot of different models attempt to address all of the issues with spatial logit models ...
1
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0answers
56 views

Calculate prediction interval for SAR model (errorsarlm function in R)

I would like to predict prediction interval for a SAR model (function errorsarlm in R - package spdep). While the function predict.lm allows to set interval='prediction' parameter to predict the upper ...
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0answers
18 views

Testing causes of accident blackspots on road network

Say I have a way of predicting, for each link on a network, the likelihood of road accidents. I also have a (mercifully quite sparse) point data set of recorded accidents. Bearing in mind that the ...
1
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0answers
38 views

Measurament error from semivariogram

I am looking at a model to determine the measurement error for a set of measurements of a spatially correlated phenomenon (sea surface temperature measurements) using a semivariogram technique. For ...
2
votes
0answers
64 views

How can I evaluate spatial autocorrelation in a binomial GLMM?

Following Dormann et al 2007 Ecography, I have employed a GLMM approach in R to account for spatial autocorrelation in a binomial regression model (logistic regression) that does not have random ...
3
votes
1answer
74 views

Is it valid to use a model-variogram fit not on the full range of lag distance?

I am trying to implement a form of 2 stage least squares, in step 1 I ignored the spatial correlation between the observations, now in step 2 I look at the spatial correlation of the residuals of step ...
6
votes
1answer
129 views

Intrinsic spatial stationarity: doesn't it only apply for small lags?

From the definition of Intrinsic stationarity: $E[Z(x)-Z(x-h)] = 0$ This assumption is used for example in ordinary kriging, instead of assuming a constant mean over the entire space, we assume the ...
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0answers
9 views

Classification for series of lat, lon points of varying lengths

I have a dataset of series of latitude and longitude points. Each series of points corresponds to some activity. The number of latitude/longitude points is not the same for the series. My dataset ...
2
votes
0answers
42 views

Universal kriging: which variogram to use?

I am working with the build in dataset meuse, which has 155 measurements of Zinc and the distance to the river "Meuse".(http://rspatial.r-forge.r-project.org/gallery/). Now I am trying to imitate ...
1
vote
1answer
40 views

How to use geometric proximity in classification

I am doing a classification of certain regions of an image. Let's say I have done the classification, and some classes have been classified positively (negatively) with high probability. For my ...
2
votes
1answer
110 views

Correcting for spatial autocorrelation in dissimilarity datasets

I have a community assembly dataset with 299 species at 15 sites. Im interested in correcting for the effect of spatial autocorrelation on beta-diversity (or species turnover). Dataset here. There is ...
2
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0answers
16 views

Multiple Imputation for Spatial Models

I'm trying to estimate various spatial models (SAR, SDM, SEM) but have missing data throughout my variables. The mice package in R gives a straightforward solution when none of the variables with a ...