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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Comparing service utilization across zip codes

I work for a nonprofit and we are trying to assess utilization of our services across our service area, particularly to identify any communities to target for additional outreach. We are working with ...
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Fitting a well-specified 1D function of time to 3D spatial and spatially correlated data

I have acquired experimental data that can be considered to be a scalar field in physical three-dimensional space, $(x,y,z)$ that I have observed over time, measured on an equally spaced regular grid. ...
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Spatial Lag returns unexpected result [closed]

I am getting an unexplainable result as my spatial lag. I read the data as follows ...
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Decreasing trend in semivariogram of nlme model residuals?

I am new to spatial statistics and am trying to fit some spatial models using the nlme package in R. I fit three different models, using the same set of predictors ...
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How to delete 1 degree of freedom from SAR model?

I have a question regarding SAR model. After computing lagsarlm model with my coefs data and matrix W1_list (showed below) my model creates many NA-s. I don't know how to remove 1 of my degrees of ...
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How to introduce panel data to spatial econometrics

I have a technical question: how to introduce panel data to spatial econometrics model, where I have SpatialPolygonDataFrames to handle my locations? I'm confused whether I should put year variable (...
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Any alternate spatial correlation/regression method than Moren' I and GWR

I have economic and health indicators for a city. I want to find spatial correlation between say mental health scores of the people with their average income. If I use Moran's I, then I can only see ...
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OLS regression - Create a map from categorical location-related variable?

With multiple linear regression in Python/statsmodels I would like to analyze quote data from the last 10 years as a hedonic price regression. The dependent and independent variables are given in the ...
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Nested, mixed-model, distance matrix, do we need all these elements for our spatial modelling?

Our aim is to model biomass as a function of distance to try to visualise a possible marine protected area’s spill-over effect. Starting from the centre point of the fully protected area (FPA), we ...
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How to analyse a continuous data which is non-linear, heteroskedastic and is spatially autocorrelated?

I have data which is non-linear, heteroscedastic and is spatially autocorrelated. The predictor and response are continuous variables. Quantile regression accounts for the heteroscedasticity but I am ...
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Would it be possible to include time-lags of independent variables in a Spatial Durbin Panel model?

In a panel setup, I have a model where all the right-hand side variables are lagged, which I am now estimating with a TWFE OLS estimator. Given that the phenomena are spatially concentrated, and I ...
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Spatial analysis / kernel density in R, what kernel describes this distribution of points?

So, I have a scenario where I want to model the probability distribution / density of random displacement using kernel density, but having trouble finding resources on how the math works. I'm working ...
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How to simulate a SAR model?

I'm trying to simulate a SAR data generating process but I'm not getting the excepted result. To be clear, by SAR model I mean: $y = \rho Wy + X\beta + e$ which can be rewritten as $y = (I - \rho W)^{-...
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Calculate probability of dying in school shooting using spatial statistics

Some public commentators say that the chance of dying in a school shooting is very low, so they conclude that Americans over-react to them. https://www.washingtonpost.com/outlook/school-shootings-are-...
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GMRF Besag model specifcation and discussion

Let $G$ be a connected graph with $n$ nodes, and let the stochastic vector $\boldsymbol{X}$ be specified to be a Besag model on $G$ with precision parameter $\tau > 0$. I want to specify the ...
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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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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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