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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Spatial autocorrelation of car crashes and traffic safety complaints in python
I am admittedly new to spatial autocorrelation and I did this research myself so apologies if this is completely noob behavior. I am trying to determine if there is a correlation between the location ...
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Accounting for spatial dependence on 2D surface embedded in 3D using GAMs
I want to infer if a dependent variable (DV) can be predicted by several independent variables (IVs). Their relationship is non-linear and monotonic so I chose to use a GAM. Using the gam() function ...
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What is the correct way to incorporate sampling weights into a spatial regression?
I want to know what the proper procedure is for incorporating sampling weights into a regression. I am running a spatial regression using survey data in R. I'm using the function ...
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R joincount with non-integer counts
I am using the R function joincount.multi from spdep to look for spatial autocorrelation with my data. The joincount column is ...
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Calculate the correlation between two Space-Time Series
What method would be employed to calculate the correlation between two space-time series?
For example, dataset 1 = a set of space-time points relating to cases of disease 1 and dataset 2 = a set of ...
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How would you deal with sparse and scattered 2d data in a way that makes physical sense?
I am currently analyzing rainfall data which is in longitude/latitude/value format i.e. a 2d matrix. That is, I have a series of values x,y,z such that ...
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How to get plot of Ripley's K function (Kest) for temporal data in R? [closed]
I have the temporal data of Landslide events in CSV format. My data consists of Year, Month, and Day of events. I want to see if there is temporal clustering for events or not by using Ripley's K ...
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Alternatives to Gaussian Process for spatial count data
I have some 1-D spatially distributed count data, and I'd like to model it as a function of a relatively smooth latent variable. I've managed to get a version of this to work using a Gaussian process ...
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test fof relative global impact of latitude and altitude on a variable, should I account for spatial dependence?
I want to test for relative global impact of absolute latitude and altitude on a variable that ranges from 0 to 1 (six possible values). I have thousands of observations sampled across the world. I ...
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How to model risk with presence-only data?
I'm working on a dataset of bird electrocutions. There are 300 instances of electrocuted birds and I have a range of environmental data that I want to input as my predictor variables. The aim is to ...
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Combining location based probabilities from GPS data
I'm working with GPS data and have billions of GPS points stored in a DB. I'd like to be able to use this to create a class likelihood function that I can use somewhere else as as a visualisation or ...
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Coefficients interpretation of survregbayes
I used the function survregbayes from the package spBayesSurv in R to perform a spatial accelerated failure time model with CAR ...
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Is there a way to automatically split large clusters that are greater than some maximum number of points?
I ran HDBSCAN on these coordinates and got some clusters but some are too large. HDBSCAN has a minimum cluster size parameter, but no maximum size. All I want is to intuitively divide larger clusters ...
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Integrating splines over a set of cells
Let $f(s) = \sum_{i=1}^p \beta_i \phi_i(s)$ such that $f: \mathbb{R}^2 \mapsto \mathbb{R}_+$ and the $\phi_i$ are splines (B-splines or Thin-plate splines) and suppose we are interested in $W\subset ...
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Comparing centrality measures of different graphs
I have a set of samples that each containing a certain number of points in 2D. The number of points varies across samples. Each point in these samples also has a specific type, like blue, red, green, ...
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Stationary spatial random filed
I am very new to the spatial model. My question is, what does a stationary spatial random field mean? If I divide the data, say, "meuse" dataset in "sp" package in"R" ...
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Is it possibe to extrapolate abundance estimates of N-mixture models to unsampled parts of a species range?
I have repeated count data for a part of the range of the species I am interested in, and have used unmarked to get an abundance estimate for these. My understanding is that the population estimate ...
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Performing Chi-Square test (or similar) across a spatial grid
I have a dataframe in R where each row is an sf-formatted geospatial grid. For each grid, I have the number of venomous snakes and the number of non-venomous snakes seen in that area. I also have data ...
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Distributions in the context of geo-spatial data [closed]
I have geo spatial data with lat, lon of user activity. I have divided this data into geohashes with precision 6. For each geohash, how do I measure the distribution of points within them?
How do we ...
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(Using conditional expectation to calculate) expected value of the product of two dependent random variables
Let $\mathbf{X}$ be Binomial point process in $W = [0, 6] \times [0, 4]$ with $n$ points. Let $A_1 = [0, 2] \times [0, 4]$, $A_2 = [0, 6] \times [0, 2]$, and $A_3 = [2, 6] × [2, 4]$. I want to find $E[...
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Time series data [duplicate]
Is the following M4_regular_matrix time series data? I am a bit confused since The rows indicate locations although the columns consist of time. We actually ...
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Spatial Regression with variables at different spatial levels - multilevel regression necessary?
Is it possible to calculate a spatial regression with variables at different spatial levels WITHOUT any aggregation/disaggregation? Or is it necessary to consider the multilevel regression?
dependent ...
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What is the correct approach to creating spatial weight matrices for a spatial lag/error model when some observations are very far from any others?
I am running a spatial regression model in R, but I am having trouble figuring out how to define a spatial weights matrix. There are two packages I can use, the package spdep has the function ...
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Does bootstrapping along time tell us anything about spatial fields?
A common problem in my field (climate science) is that we want to assess the statistical significance of a 2D spatial field. This field is typically obtained by taking the temporal mean of a subset of ...
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Global and GWR models
I have mortality data for municipalities in my country and I would like to know if an approach such as modelling with a Linear Regression and if the assumptions like normality, equal variance and more ...
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Name for matrices with $M_{i,j} = M_{n-i+1, n-j+1}$ symmetry (eg. 2D autocorrelation)?
Covariance matrices are symmetric positive definite matrices. The 'Symmetric' part of this means that the strict upper elements are redundant with the strict lower elements, i.e $M_{i,j} = M_{j,i}$. ...
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Creating a Variogram-like plot with distances
I have a dataset of points in space, sampled at a specified time. Now i want to obtain a 'spatial correlation plot, only between points close in time'.
What i did is creating a dataframe of pairs of ...
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Intersection of matern covariance functions with different parameters
I am trying to prove some ideas about the identifiability of covariance function parameters for small samples. From various numerical experiments, it seems that the graphs of two matern covariance ...
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A hard core spatial point process that a vehicle encounters as Poisson arrivals?
Does there exist a point process $X$ in the plane with the following two properties?
$X$ is hard core. Discs of radius $h$ can be centered on the points in $X$ without overlapping.
$X$ is ...
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Comparing Defects per Volume
Let's set up a toy problem. Say I make a vat of soup every week. Different kinds of soup, different volume each week (e.g. 50 gallons, 46 gallons, 10 gallons, etc.) and sometimes there are flies in ...
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Spatial autocorrelation machine learning python
I am trying to build a machine learning model for spatiotemporal data. My predictors are all monthly climate variables, and as such display spatial autocorrelation. The target dataset however has ...
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How to choose between a count model and spatial error model
I have a dataset with spatial grid cells. After performing some spatial diagnostics (Moran's I, LaGrange multiplier tests), it seems that there is spatial dependency in the form of spatial error, so a ...
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Analyzing geolocation and time of purchase data to determine the spatial provision strategy for a new product
I have a dataset containing geolocation and time of purchase information for buyers of a new product (Product X). I'd like to determine the unit of spatial provision when this product was offered to ...
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How to apply spatial correction to field data based on many randomly dispersed check varieties with varying levels of replication?
I have inherited a dataset with a truly chaotic trial design. 32/155 varieties are duplicated at least once, most having 2-4 reps, but one is repeated x5, another x8, and another x19. I was told that ...
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Spatial autocorrelation correction with glmmTMB
I am currently working on a dataset (count data) in which one observation corresponds to one day of monitoring at a site.
The overall protocol is to monitor groups of sites along transects. Almost all ...
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How best to statistically compare radial intensity curves?
I've the following data.
https://drive.google.com/file/d/1_a-mi_Pesx73AnvbKVNr4SzhuujKq9Za/view?usp=sharing
These .csvs contain logged radial intensity curves to a distance of 2.2 cm of different ...
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Modelling monthly data from annual raster data and monthly point sources
I have raster data that provides annual air quality measurements over my study area (Toronto). I also have monthly air quality data from four monitoring stations in Toronto. One of my jobs is to ...
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How to provide data for the "PROX" argument in BayesSAE R-package (To Model the Spatial Fay Herriot SAE)
I am trying to apply Fay Herriot (FH) with Spatial structure (CAR=Conditional Auto Regression) using BayesSAE Package in R (here is the link to the package: https://cran.r-project.org/web/packages/...
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In my DiD regression, I have % change as dependent variable and log(x) as a regressor. How do I interpret the beta coefficient?
I am trying to study how rent changes has evolved over distance to the city center between the pre- and post-COVID time periods. So I devote particular focus on comparing the relative rental and house ...
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Bayes factor for testing fit of different spatial models in Stan
I have a question about testing of different spatial specifications of a polynomial regression. I am using Stan for Bayesian inference, but I did some initial exploration using the R packages ...
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score distribution of categorical values in rectangular 2D plane: is Moran's I my best bet?
I need to distribute points of 3 different kinds in a rectangular 2D plane so that they are spread out as evenly as possible. That means, I want to avoid "gatherings" of points of the same ...
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How can I build a 6D Convolution Layer in a CNN, using R or Python? [closed]
As stated in the title, I would like to build an N-dimensional Convolutional Layer as part of a Convolution Neural Network, without doing dimensionality reduction on my data; because I have multiple ...
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How can I simulate the spatial clumpiness of ocean observations?
I have a dataset of ocean observations which are very spatially uneven. As they’ve been collected from research ships they’re very “clumpy” (see top figure below). For simplicity, let's say they're ...
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What is a spatial modeling approach that works with complex survey design, where multiple observations share single point location
I'm working with the Demographic and Health Surveys data, which is a nationally representative household survey that uses a complex, two-stage cluster sampling design. In essence, this means that ...
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How to evaluate impact of one group of items (spatially scattered) on the other group (spatially scattered)
I have two groups of data, the green dots group represents the emissions of a type of air pollutant, and the red dot group represents the deposition of the air pollutant. Each green/red dot has a set ...
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Apply geographically weighted regression's model parameters to a finer spatial scale [closed]
I have three raster layers, two coarse resolution and one fine resolution. My goal is to extract GWR's coefficients (intercept and slope) and apply them to my fine resolution raster.
I can do this ...
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Spatial Statistics (for mathematicians) lecture notes request
I'm currently doing a master's in mathematics, and I'm interested in spatial statistics, mainly for a project. Unfortunately, it seems impossible to find lecture notes on this topic. I have seen some ...
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Simulation envelopes for Foxall J not containing the line y=1 (fully reproducible example)
During some exploratory analysis of my data I noticed that when Jfox was calculated for a point pattern relative to some polygons, I frequently obtained that the pooled envelopes area did not include ...
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Spatial cluster analysis with non-normal data using Moran's $I$
I need an appropriate method for quantifying spatial spread (i.e., clustering), where area of interest is the country of India divided into 25km$\times$25km grid cells, and the response variable is a ...
