Questions tagged [spatial]

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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How would I separate a data set into a large number of pairs?

I have $n$ observations in $\mathbb{R}^2$ (where $n$ is large and even), representing points in physical space. What I am trying to do is to produce a set of $\frac{n}{2}$ pairs, such that the ...
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How to statistically compare two numerical maps? [closed]

I have spatial temperature data on a relative scale from 1 to 0 on several days. The data sets have the same resolution, origin, and extent. How can I statistically compare them? How to identify ...
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pointwise envelopes not including Theoretical line Foxall J

I am computing pointwise envelopes for the Foxall's J function to investigate the whether some point patterns of interest are clustered, avoid or are independent from other point patterns or polygons. ...
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Poisson process for the spatial analysis of accidents

I have a large dataset consisting of the geographic location, company, and date of accident. I also have a grid with a cell size that is 6 miles x 6 miles to disaggregate the data, since the dataset ...
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Need to merge yield data for each GDMID with shape fie and plot the map filled with yield

I am trying to plot an Indian district shapefile filled with modeled yield, corresponding to each GDMID, common in shapefile and .rds dataframe. I need to first merge the Yield data with respect to ...
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Spatial clustering with maximum group weight

I am looking for a clustering method that would allow spatial clustering of a set of points (with weights associated to each point) with maximum cohesivity, where each group of points must have at ...
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Algorithm for generating a Poisson process on a complicated 2d geometry

I am looking at some count data by geographic counties in California. As a starting point, a Poisson process came to mind--though there are other good choices like negative binomial, etc. Given a $\...
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How Is Kulldorff spatial scan statistic protected from multiple testing bias?

I recently discovered Kulldorff spatial scan statistic which is used to identify disease clusters in a Poisson or Binomial process: https://sci-hub.se/https://doi.org/10.1080/03610929708831995. It ...
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Do spatial regression models hold the assumption of linearity?

I'm running a spatial regression model (probably spatial durbin error model or spatial durbin model) and one of the explanatory continuous variables has a lot of (true) zeros. I'm worried that the ...
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Actual residuals versus simulated residuals for testing spatial autocorrelation

I have a dataset of observations at multiple sites repeated within and at multiple times. I need to test for spatial autocorrelation in the residuals of my model, which I have used time as a fixed ...
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Spatial autocorrelation with multiple observations per site

Part 1) I have a dataset with response values of 0, 1 or 2 which has multiple observations per site as they have been unevenly repeatedly sampled through time. When calculating spatial autocorrelation ...
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Geographically Weighted Regression Model Selection - independent variable is a discrete count and dependent variable binary. is this possible?

I'm investigating spatial variation in the relationship between a certain type of building development and gentrification - Simply, my research question is as follows: is the outcome of gentrification ...
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I need guidance regarding a permutation based distribution problem

I am conducting a semester project that involves evaluating proportions of landcover (from NLCD) within animal survey areas (1km grid cells). A professor suggested that I randomly sample proportion of ...
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How to fit overdispersed count data in R using INLA package?I have tried the Neg Binomial family already! I would like to use other family in R?

I would like to know how to fit a spatial conditional Poisson and Neg Binomial in R using INLA to cater for overdispersed counts data.
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Spatial crosscorrelation between a binary and continuous measurement on a graph

I have a graph $G$ with vertex set $V$ and edge set $E$. I measure two signals on the vertices of the graph $X$ and $Y$. If $X$ and $Y$ are both continuous, we can measure spatial crosscorrelations ...
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Expectation and Variance of Moran's I under the Null

Moran's I is a statistic used to measure spatial autocorrelation. For a set of $N$ spatial units where we get measurements $\mathbf{x} = (x_1, x_2, \cdots, x_N)^T$, and a weight matrix between the ...
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Resources on Modeling Regional Count/Rates

I'm looking into the time-evolution of regional count data. The real target is modeling death rates at the county level over time. I was wondering if people had suggestions on where to start reading ...
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R package for spatial regression

I want to model counts of a beetle (B) as a function of three continuous dependent variables (PC1, PC2, RL), weighted by a fourth variable (W) using either poisson or quasipoisson regression with ...
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How to deal with spatial autocorrelation AND temporal autocorrelation in a mixed model?

When creating a mixed model (or GLS) with spatiotemporal data, you can include correlation structure into your model to address autocorrelation. Spatial autocorrelation can be modelled for with ...
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Breakdown Regional Data into City-Level data

I have average level household- income data of 26 statistical divisions of Turkey (NUTS 2). I need to break down and estimate this data to city (81 sub division NUTS 3) or town level (LAU) level to ...
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Quantifying spatial correlations for independent images

I have a series of maps (100x100 pixels each) and for each map, each pixel is labeled according to what is located there (currently values are in RGB, where each color denotes a different substrate). ...
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Clustering large yearly, (presence/absence) dataframe

I have a data frame of 500,000x23 dimensions. The data is binary, representing presence or absence. The data follows identified trees through time (23 years) and looks at whether the tree is present ...
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What is the best spline smoother to use with spatial data in a GAM?

I am running a GAM and need to account for spatial autocorrelation. I have done this by including "s(easting,northing, bs="gp")". I was wondering if gaussian was the best spline ...
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Using ground-truth values to update predictions of trained model

I have a geospatial machine learning problem involving training a machine learning model to learn to predict the value of ground truth data from satellite images. What I am hoping to do is be able to ...
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INLA model with spatial autocorrelation - binary response, binomial predictor

I have a binary response variable (0/1) and a predictor distributed continuously on 0-1 scale. There is significant spatial autocorrelation in data, thus I am running an INLA model to account for that....
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Interpreting an OLS regression where treatment is spatially associated to confounder

I am reading a study that is running an OLS regression to assess the effect of a treatment ($T$) on an outcome ($y$). Outcome $y$ is a disease, and the treatment group is strongly spatially associated ...
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How to check assumptions and model specifications for spatial regression analysis in Stata?

I want to run several spatial autoregressive regressions (SAR) using the "spregress" command in Stata (version 16.1). When you do non-spatial multiple OLS, several assumptions have to be met,...
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Spatial Verification Techniques for Image Prediction

I am working on an image prediction problem, where we use a U-Net to predict a real-valued image. I've found that conventional metrics like MSE, r^2, MAE, etc just don't really cut it. What are some ...
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Measure "dispersion" of geospatial data

I have a dataframe of lat and longs (of postal codes of Canada), let's say they look something like this. I'd like to come up with a measure of dispersion - basically how spread out these postal ...
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Which model to use for space-time classification?

in my current dataset I have 30 years of max daily temperature history for the whole USA area (points on a lon/lat grid, each point is 10 km apart). I would like to predict if a max daily temperature ...
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Spatial and time lag autocorrelation

I am designing an experiment to examine spatial autocorrelation within 50km of a point. The setting is the open ocean with sensors that take depth profiles of environmental data (Temperature, Salinity,...
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Struggling to make sense of coefficients in production-constrained spatial interaction model (Poisson)

I'm having a hard time trying to understand what is the meaning of the coefficients of a production-constrained spatial interaction model re-specified as a Poisson linear regression model. Following ...
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Compare geographical distributions of prevalences

I would like to ask you what statistical method should be used to compare two prevalence distributions. My problem is the following: I have two prevalences of two diseases A and B (non-exclusive) per ...
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Moran's I and mixed effect model correlation error structure

When creating a mixed model with spatial data, you need to check that any model isn't being influenced by spatial autocorrelation. To my understanding, the most robust way of doing this is by ...
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5 votes
1 answer
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Interpreting a formula on geographic concentration

The calculation formula of geographical concentration is as follows: $$G=100\times\sqrt{\sum_{i=1}^n\left(\frac{n_i}{N}\right)^2}$$ The explanation of the formula is something I don't understand: In ...
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Which approach to evaluate small spatial variations in temperature?

I am evaluating whether utility-scale solar energy plants affect the surrounding climate (initially temperature). An effect has been found in one paper using the approach described below/attached but ...
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4 votes
2 answers
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How do I combine predictions of four Poisson regressions that use the same independent variable?

Question: I have a large area, $N_{total}$, where I decomposed spatially into smaller $i$ squares (northwest, northeast, southwest, and southeast quadrants) and ran Poisson regression on these four ...
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Will ANOVA work for spatial data?

I have 16 variables separated into columns with 12 observations each. Each row represents a point along a sampling transect, so for example, A2, B2, C2, and so on are all different measurements but ...
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moran's test using residuals of linear mixed effect model

A follow up question to Moran's test using the residuals of logistic regression Is this doable on linear mixed effects models (lme)? I want to use Moran's I to determine if accounting for ...
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How to interpret level set threshold for the posterior spatial random effect from a Log-Gaussian Cox process?

For a Log-Gaussian Cox process, the prior distribution is a zero-mean Gaussian process. But for the posterior distribution, it is analytically intractable and it is not a Gaussian process anymore. So ...
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1 vote
2 answers
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Modelling spatio-temporal data with repeated measurements

I would like to perform regression on an environmental dataset. The covariates are in the following form I realize that the 4 repeated measures for each region are dependent because they come from ...
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Determine if spatial point set is uncorrelated

What practical techniques are there to determine if a spatial point set is uncorrelated, i.e. if the positions of the points are independent of each other? Are there techniques which work robustly ...
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Appropriate statistical procedures to compare sales based on Internet search keys

This is my first question ever on Stats StackExchange and I am hoping to get some guidance from the community. I have graduated with a BSc in Statistics, so I am familiar with the fundamentals of ...
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Spatial data analysis

I want to use a probit model on the probability of occurrence of a social problem in which one determinant will be location. For this, I have data on locations where the problem is occurring. What ...
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3 votes
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At what spatial scale should PCA be analysed on? Why do the loadings appear so different at each scale?

My dataset has 6 sites. Each site has four quadrants (qi) that I sampled for 12 months to estimate species abundances. I Hellinger transformed the data prior to the analysis. For each quadrant I have ...
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1 vote
1 answer
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Contour plot comparison [closed]

What are the ways to compare two different maps? I was thinking of taking the matrices of each map and then quantify their spatial differences by plotting the map of the differences of the two ...
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How to perform PCA analysis on space-time data for few species in R? [closed]

The data-set I'm looking to analyze has 6 sites. Each site was sampled at five unique locations each month for a year. We could identify abundance of 4 species across the data set. Together, I have ...
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1 vote
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Residual autocorrelation in non-stationary (gaulss) model

I'm fitting a non-stationary model using mgcv (family: gaulss()) where the data have been collected at different points in space....
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Incorporate spatial constraints in K-means clustering in R language

I have incorporated spatial coordinates (e.g. longitude, latitude) as additional features within a dataset that will be clustered based on K-means algorithm. However, I would like to apply spatial ...
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1 vote
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Orthogonal basis functions in `mgcv`

I have a spatial dataset that I'm modeling using penalized splines from mgcv. Here's a simplified example of what it looks like: For simplicity, I'm modelling some ...
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