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Questions tagged [geostatistics]

Statistical modeling and analysis of georeferenced data, often modeling spatial covariance. An example is kriging.

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EM-algorithm for spatial data

I am very new to Geostatistics (Modeling spatial data) and have some questions: 1- I found that in many literature, the spatial random field is divided into spatial bins. That is, suppose I am ...
Dr. Statistics's user avatar
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Enforcing symmetries "for bag-of-vector" data in XGBoost or random forest - geodata example for illustration

I'll give a concrete toy problem, then give some comments on what sorts of abstractions I care about. Toy problem: Each person $i$ in my dataset has a phone, and every once in a while the phone will ...
user1557414's user avatar
2 votes
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140 views

GAM model with spatial account via MRF

I am starting to read a little about generalized additive models and how spatial dependencies can be incorporated into the model, I'm reading Generalized Additive Models An Introduction with R from ...
Raheshi Knuwga's user avatar
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Imputing missing observations of zip code level data

I am looking for a sufficient imputation method for missing observations in my zip code level data, using R. I have a random sample consisting of households which live in different zip codes within ...
Ottibanane123's user avatar
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Can we apply geostatistics on data which is not normally distributed?

If we work on groundwater data, if the data is not normally distributed, can we apply geostatistics on this data without transforming it to normal distribution?
Araminta Neog Bharali's user avatar
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Statistical Integration of Bayesian and Frequentist Approaches: Weighing Methodology

I'm uncertain about where to post this question. I'm currently working with geotechnical data (soil parameters) and aiming to obtain realistic and safer parameter values. To achieve this goal, I've ...
JCV's user avatar
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Statistical test whether data conforms to a spatial point process--gaza bombing locations

I came across this image on twitter, and it made me think about testing a point process hypothesis. Now this is a politically sensitive image, and I don't want to run afoul of any SE posting ...
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Compare and aggregate indicators at different geographic scale

I encountered a problem in developing an aggregation model of several indicators. Almost all the indicators are at a small geographical scale (admin2 / Provinces), but one is at larger geographical ...
Dorianeve's user avatar
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Larger vs smaller areas for analysis: how to make a choice?

I am researching the composition of labour force, and my variable of interest is the share of workers in a specific occupational category. I measure it by parish and also measure a set of explanatory ...
Mikhail's user avatar
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How to model user behavior with a time series of discrete locations in continuous time?

I have a dataset that contains the sequential locations a user has visited, with start and end times. A small sample: ...
sander's user avatar
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Geostatistics: Covariance vs Semivariance

I am confused by the following page in Geostatistics for Environmental Scientists, Webster & Oliver: My understanding Given locations specified by a vector $\mathbf{x}$, we assume an underlying ...
Mr Lolo's user avatar
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Spatial analysis: How to find region most prone to storm loss when only have loss data for whole region?

I'm thinking about a statistical problem and I would be grateful if you could give me some hints of how I could address this problem: I have data of n insurance companies and for each insurance ...
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Covariance estimation in simple kriging: the "dead end" problem (authorization of using variogram)

I am confused about the presentation of using variogram in simple kriging in the book of Multivariate geostatistics by H. Wackernagel (1995). I understand the process of derivation of simple kriging ...
hamsa's user avatar
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Declustering spatial data and kriging

I am doing a simple project on a mining dataset and came across some problems: Should I use the declustered values to model the variogram? Do I apply ordinary kriging to the original or to the ...
Gustavo Scholze's user avatar
1 vote
3 answers
293 views

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 $\...
krishnab's user avatar
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How to constrain ordinary kriging weights to sum to 1 in R

I need to perform ordinary kriging on a dataset and I understand that I need the weights to sum to 1, I just don't understand how to set that up properly. For example, my covariance function is C(h) =...
Ric's user avatar
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Showing RMSE and MAE results as percentage error

I have the results of RMSE and MAE from different spatial interpolation methods as a monthly averages (See the figure below). As ...
Mukhtar's user avatar
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Is inference about interior bear population from observation boundary an un"bear"able estimation problem?

The following map shows a density-coloured grid of observations of American black bear reported on iNaturalist. In the top half of the province you can see a loose boundary of observations around an ...
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Estimating probability of attack in Ukraine, given count data

I was looking at some attack count data in Ukraine for different days. The data is gathered from the ACCLED dataset, and there is a picture below. The picture shows individual attacks, but I can apply ...
krishnab's user avatar
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3 votes
2 answers
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Can I simulate a random field with nested variogram by a summation of independently simulated random field for each componnet of the nested variogram?

I want to simulate a random field $Z(u)$ that has a nested variogram, say $\gamma(h)=\gamma_1(h) + \gamma_2(h) + \gamma_3(h)$, assuming the variogram is isotropic. Whether can I simuate independently ...
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Whether the correlation structure of random field $Z(u) + Y(u)$ is equal to the correlation structure of $Z(u)$ plus that of $Y(u)$?

I want to simulate a Gaussian random field (RF) with correlation structure (represented by the geostatistic tool 'semivariogram' $\gamma (h) \: +\: pure \: nugget \: effect$). I want to know whether ...
tunar's user avatar
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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 ...
Winston's user avatar
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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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Quantifying magnitude of change in dataset containing base values of 0?

I have a dataset with the low-water and high-water surface area of lakes/ponds within a delta for each year obtained from satellite imagery. These lakes vary substantially in surface area and can ...
Nic's user avatar
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Parametrization of the Matérn Covariance Function

For geostatistics problems, I am used to working with the following parametrization for the Matérn covariance function. For a stationary and isotropic Gaussian random field $X(\boldsymbol{s})$, $\...
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How could I even try to analyze map data? [closed]

I have a situation where I'm trying to analyze how users interact with maps. Here's an example to illustrate: Below is an image of Google maps after searching for Chicago, IL pizza. Now, say that I ...
andy_d's user avatar
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283 views

calculating b from the Gutenberg-Richter distribution [closed]

I'm trying to calculate b from the Gutenberg-Richter distribution though, I'm struggling in understanding the calculation. For example, the equation is this: $Log_{10}N(M) = a-b(M-M_1); M \ge M_1$ $...
Meilton's user avatar
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How to calculate the interannual variability and Seasonal Amplitude?

I have a time series of remote sensing observables. For example, the observed brightness from 2007 to 2019. And we have 2 datapoints per day. How can I calculate the interannual variability of this ...
Xu Shan's user avatar
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1 answer
674 views

Cokriging and collocated cokriging data requirements

In this wiki article and elsewhere in educational materials/papers, I have seen people refer to the idea that secondary data, if used (appropriately) in cokriging or collocated cokriging, is usually ...
Denys D.'s user avatar
19 votes
3 answers
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Data partitioning for spatial data

I am constructing different configurations of a Random Forest in order to investigate the influence of well-design variables and location, on the first-year production volumes of shale oil wells, ...
veghokstvd's user avatar
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Integrating logistic regression with CA to simulate land cover after prediction

I have used logistic regression in R to predict the probability of change in 3 types of land cover, the map of probability is generated for my case study. I do not know how to use the probability ...
Shae's user avatar
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1 vote
2 answers
132 views

Which is the best model for explaining spatial distance between points?

I have a dataset with distances between beneficiaries and the nearest provision point (nearest hub). I want to develop a model to explain distances based on several atrributes like category of ...
Josep Pueyo's user avatar
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29 views

How to predict the time of next trajectory for users

I am working with the Geolife dataset and would like to create a statistical model to predict when users will go on their next trip. The implication is to see if we can build a model to be able to ...
Brandon Segal's user avatar
4 votes
1 answer
154 views

Why do we treat a value at each location as a random variable?

Apology if my question is very simple to you (I am very new to geostatistic). Suppose that X is a random variable (e.g., the concentration of the Zinc at a specific ...
Mary's user avatar
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2 answers
425 views

How can I create a map of variance for IDW predictions?

Unlike with kriging, predicting spatial variation using the IDW function from gstat returns only predicted values, not estimates of variation. ...
Nat's user avatar
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3 votes
1 answer
33 views

How to use regression of data linked in space and over time?

I have an R data frame, set up like so: ...
aeiche01's user avatar
2 votes
1 answer
536 views

Is the spherical covariance function not positive definite for d > 3?

I read in a textbook (Japanese one) that the spherical covariance function is only valid for dimensions $d = 1,$ $2,$ and $3.$ I have the following questions: Does that mean the spherical covariance ...
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Cokriging variances differ using cross validation

I'm investigating cokriging using various metals in the Meuse dataset but the variances output by R when I predict values at gridded points differ substantially from the variances produced by cross ...
Ben Collister's user avatar
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1 answer
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What are the possible machine learning models for this geospatial analysis task?

I am new to ML and have some experience with building CNN models. I recently got involved with a research project and here is the task I have to work on: I've been given some (latitudes,longitudes) ...
Divyansh's user avatar
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2 answers
145 views

Zonal statistics (mean± StDev) from polygons with different area

I am analyzing a temperature image using a land cover map with the overall goal to prepare a barplot where I report the mean ± StDev temperature for each land cover class. To do so I assigned each ...
pan's user avatar
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2 votes
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161 views

Correlating Two Time Series with Gaps in Data and of Different Lengths

I am attempting to correlate the time series from 4 separate tilt monitors that sample every 5 minutes. The time series have slightly different base times and end times, and the resulting arrays are ...
geojasm's user avatar
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1 vote
1 answer
52 views

Controlling for spatial confounding in point-referenced data

I have a point-referenced data set with 2 binary outcomes. The data shows a strong correlation between these binary attributes - however, the geographic clustering is also qualitatively clear. I would ...
Simon F's user avatar
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2 votes
1 answer
40 views

Finding the effect of nodes on a density heatmap

Let's say I have a geo-tagged dataset of all payment transactions for businesses in a city. I know whether each payment is made by cash or card, and have made a heatmap of where in the city the ...
NeonBlueHair's user avatar
2 votes
1 answer
124 views

Return Period and Probability

My question is simple: If I assume that the probability to be hit by a lightning strike for a person in this year was 0.5 percent would it mean that if I was able to live 200 years I would be hit by ...
www.pieronigro.de's user avatar
2 votes
0 answers
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Using Markov random field spatial weights to account for spatial autocorrelation

I am looking at the relationship between life expectancy and smoking rate within the London boroughs. I thus created a bayesx spatial regression model including a term which assigns spatial ...
Steve Ahlswede's user avatar
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2 answers
59 views

Can I create a test dataset with known errors to validate accuracy assessment? [closed]

I developed a procedure to measure the geometric accuracy of 3D building models based on the similarity to a 3D point cloud. Therefore I created mainly two quality criteria. The result of my automatic ...
conste's user avatar
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Statistical analysis of polygons QGIS : find areas of statistical difference

I have polygons for the country divided into E.D. areas. These polygons illustrate the average annual rainfall in their ED area. Is there any way to statistically tell if one polygon area has a ...
Will.S89's user avatar
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3 votes
1 answer
640 views

How can I understand these variograms?

Using grf function from R package geoR, I simulated 6 replicates (each with 1000 samples) of a Gaussian random field on ...
Zheyuan Li's user avatar
2 votes
0 answers
105 views

Anomaly detection with geo spatial data

I have geo spatial data. Dataset represents concentration of something. For instance, social media activity of people in point [time, lat, lon] I have some list of feature for every points (lat, lon):...
Dmitry Shepelev's user avatar
1 vote
0 answers
28 views

Cerioli's modified test of indepence with spatial data

I have to two datasets $X$ and $Y$ with binary observations on a regular grid. A regular contingency test for independence would be flawed by the presence of spatial autocorrelation. Cerioli (1997) ...
Christian Zang's user avatar