Science topic

# Spatial Analysis - Science topic

Geographical Analysis, Urban Modeling, Spatial Statistics
Questions related to Spatial Analysis
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I have calculated two cross-classified multilevel models where the models have different sample sizes. I was wondering whether I can compare (like say higher or lower) ICC between these two models.
Another question, from a cross-classified multilevel model, we can calculate the ICC index for different contexts (such as students living in the zone but school zone is different, student's school in the same zone but residence is different). At least can we add the ICC index to say that this total amount of variation is due to spatial dependency?
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Especially in how to distribute porosity and permeability of the reservoir using SGSIM of Kriging. I tried to run the software, yet, it always fails. Also, I couldn't find the tutorial on YouTube. Do we need to have the data for the cluster or we can skip that step?
Aahed Alhamamy Thank you for the answer, it really deepens my understanding of the software. And indeed that there are only a few tutorials on YouTube.
I believe that I need some data set as you mentioned because from one tutorial I watched, he used the data set but didn't explain anything about it.
However, I am not familiar with the data set for SGSIM, can you provide me suggestions regarding this issue?
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I am trying to run a spatio-temporal autoregressive model (STAR). Therefore I need to create a spatial weight matrix W with N × T rows and N × T columns to weight country interdependencies based on yearly trade data. Could someone please tell me how I to create such a matrix in R or Stata?
Dear Jan,
OK ! I see ! you need to create the spatial weight matrix indeed !
There are many possibilities in R:
I strongly advise to work with sf because it so easier now !
but spdp may still be clearly adaptated to you context :
This is one of the definitive books on the subject in R:
with the book :
there are other references but they are more geospatial (point process) oriented.
Here you should use one of those packages and that nb2mat from package spdp might do the trick !
All the best.
Franck.
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A Moran I (spatial autocorrelation) has been prepared in Arc Map 10.4 and GeoDa for comparison. Please find attached those results for your valuable input.
The main difference between Moran I in ArcMap 10.4 and GeoDa is the way that they calculate the spatial lag. In ArcMap 10.4, the spatial lag is calculated using a queen contiguity matrix, while in GeoDa, the spatial lag can be calculated using a variety of different matrices, including queen contiguity, rook contiguity, and k nearest neighbors.
The queen contiguity matrix is a binary matrix that indicates whether or not two cells are adjacent to each other. The rook contiguity matrix is also a binary matrix, but it only indicates whether or not two cells are horizontally or vertically adjacent to each other. The k nearest neighbors matrix is a weighted matrix that indicates the distance between each cell and its k nearest neighbors.
The choice of spatial lag matrix can affect the results of the Moran I test. For example, if the spatial lag is calculated using a queen contiguity matrix, then the Moran I test will be more sensitive to spatial autocorrelation that is present in the data. However, if the spatial lag is calculated using a rook contiguity matrix, then the Moran I test will be less sensitive to spatial autocorrelation that is present in the data.
In addition to the choice of spatial lag matrix, the results of the Moran I test can also be affected by the size of the spatial window. The spatial window is the area around each cell that is used to calculate the spatial lag. The larger the spatial window, the more likely it is that the Moran I test will detect spatial autocorrelation. However, the larger the spatial window, the more likely it is that the Moran I test will detect spurious spatial autocorrelation.
It is important to choose the spatial lag matrix and spatial window carefully when conducting a Moran I test. The choice of these parameters can have a significant impact on the results of the test.
Here are some additional tips for conducting a Moran I test:
• Use a variety of different spatial lag matrices and spatial windows to see how the results change.
• Use a robust Moran I test to reduce the impact of outliers.
• Use a Monte Carlo simulation to assess the significance of the Moran I test.
I hope this helps! . Please recommend my reply if you find it useful .Thanks
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Hi, in the Brightness temperature calculation, I saw two values for converting to Celsius.
Which one is correct? why in some articles use 272.15 and other articles use 273.15??
BT = K2 / ln (K1/ Lλ +1) -272.15
or
BT = K2 / ln (K1/ Lλ +1) -273.15
The correct value to use in the brightness temperature calculation is -273.15. This is because the Kelvin scale is based on absolute zero, which is defined as -273.15 degrees Celsius. Therefore, all temperatures in the Kelvin scale must be converted to Celsius by subtracting 273.15.
The reason why some articles use 272.15 is because they are using the Celsius scale instead of the Kelvin scale. The Celsius scale is based on the freezing point of water, which is 0 degrees Celsius. Therefore, all temperatures in the Celsius scale must be converted to Kelvin by adding 273.15.
However, it is important to note that the brightness temperature calculation is typically used in remote sensing, where the temperatures are measured in Kelvin. Therefore, it is important to use the correct value of -273.15 when converting brightness temperatures to Celsius.
If you find my reply useful , please recommend it . Thanks
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#research#urban-planning#users' health and sustainability
I appreciate the time and effort you have taken to share your knowledge with me.
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I did some thermal maps of the Girona’s atmospheric urban heat island with a method of car's mobile transect. I use the software Surfer 6.0 to do the maps, but Surfer isn’t a Geographical Information System, I think this is a throuble. Also in my transects there is a very high spatial density of observation points in the downtown of the city of Girona (15/km2) and a low density in rural areas (2/km2). I always interpolate isotherms with the kriging method. What is the best method to interpolate temperatures (Kriging, IDW, etc.) in my area of interest, Girona and it environs? Can you give me bibliographic citations?
It is important to note that the choice of interpolation method depends on the specific characteristics of the data and the study area. However, in general, kriging is considered to be one of the most accurate methods for interpolating temperature data.
In your case, since you have a high spatial density of observation points in the downtown area and a lower density in rural areas, it might be useful to use a spatially adaptive interpolation method such as kriging with varying local means (KVL) or regression kriging (RK). These methods can account for the varying spatial patterns of temperature across the study area.
Here are some references that might be helpful:
• Stein, A. (2012). Interpolation of Spatial Data: Some Theory for Kriging. Springer Science & Business Media.
• Oliver, M. A., & Webster, R. (2014). A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena, 113, 56-69.
• Hengl, T. (2009). A practical guide to geostatistical mapping. Office for Official Publications of the European Communities.
• Zhang, Y., & Li, W. (2014). Spatial interpolation of temperature using kriging with varying local means. International Journal of Applied Earth Observation and Geoinformation, 27, 36-44.
• Li, X., Li, J., & Li, Y. (2019). Comparison of interpolation methods for temperature in the Yellow River Basin. International Journal of Remote Sensing, 40(8), 2961-2979.
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Dear all,
I have three 100 m2 plots subdivided in 100 subplots of 1 m2. I have counted, in each subplot, the total number of individuals of a plant species, and I repeated the sampling 7 years. I would like to study the distribution (aggregation) patterns of individuals within the main plot and see if there are changes over time. I've been looking for examples with SADIE methodology (Spatial Analysis by Distance Indices), but I don't know if it fits my study. Any recommendations for studies of this type? Is there any R package to apply the SADIE methodology?
I would be thankful to all ideas.
Thank you and best regards
Hazrin Hasim Thank you for your reply. I will explore the packages you cite.
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Anybody got a good source?
Terima Kasih you for your take, Hazrin.
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I produced three different raster layers interpolating points collected in-situ in three different months in the same area using Kriging. What is the best way to highlight the temporal changes that occurred in the test site during this time? I did the standard deviation of the images, since it permits to summarize the variations in just one image. If I did the difference between subsequent images I produce two images to represent the same results. Am I right?
Performing change detection of interpolated datasets involves comparing two or more raster layers to identify areas where changes have occurred. Here are the general steps you can follow:
1. Preprocessing: Preprocess the datasets to ensure that they are spatially aligned and have the same resolution and extent. You can use the resampling tools in ArcGIS or other GIS software to resample the raster layers to a common resolution and projection.
2. Difference layer: Create a difference layer by subtracting one raster layer from another. The difference layer will show the changes that have occurred between the two datasets.
3. Thresholding: Set a threshold value to determine what magnitude of change is significant. For example, if you are detecting changes in vegetation cover, you may choose a threshold based on the percentage change in vegetation index.
4. Visualize and analyze the change layer: Visualize the difference layer to identify areas where significant changes have occurred. You can use GIS tools to analyze and quantify the changes, such as calculating the area of change or generating statistics on the magnitude of change.
It's important to note that change detection of interpolated datasets can be challenging because interpolation can introduce uncertainty and artifacts in the data. Therefore, it's important to use appropriate interpolation techniques and validate the interpolated datasets before performing change detection. Additionally, understanding the limitations of the data and the assumptions made during the interpolation process can help to interpret the results accurately.
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Hi,
I have 26 sampling locations, relatively well distributed, but not adequately enough to cover the study area. I have also a digital elevation model, from which I can extract much more locations. An important detail is that the temperature data are distributed in low elevations, but no temperature data is available at high elevations. Hence, I want to use the elevation data to sensitize the temperature interpolation process through co-kriging, which works with non collocated points. I prefer to use gstat, but I am not sure: a) what is an optimal number of elevation points that I have to use (I know that I have 26 temperature points, and that I need elevation points at high elevations) b) is there an automatic process tha provides reasonable kriging predictions, such as autokrige, for co-kriging?
You don't mention the specifics of how large your area of interest is (i.e. a 1 sq km, 10 sq km, 1000 sq km, 10000 sq km, etc.) or what exactly 'low' and 'high' mean, along with the baseline those are stating from, or where in the world it is - which would indicate the orography and general texture of the terrain relief. Perhaps extreme, but in my neighborhood, that can range from 2000 ft to 14,000 ft in 18 miles - 50 miles east that same distance has a range of about a 100ft, not 12,000 ft of elevation. Also you don't mention the chronological aspects and intervals.
Merely creating some sort of interpolated surface between your samples, depending on the above is self referential and artificial. The actual 'surface', again depending on the area of interest, is another 'DEM', grid data from the weather model of the region appropriate for the AOI which captures the general trends in the AOI.
Also, there is another correction, depending on your data source, it is not only the measured temperature on the interpolated surface, but the Density Altitude relationship correction, which will vary across the surface. Even very small variation in pressure, temperature, and altitude can have influences of a thousand feet - this life or death correction is done immediately before every airplane flight.
These aren't the only factors, and whether they matter of not depends on the extents and sampling of your area of interest, and your analytical goals.
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I need layers for spatial analysis of Sakarya province in the eastern Marmara region of Turkey. Where can I find it for free?
HI Melis, good luck. I hope you find what you require. Sometimes, if you need specific data at high resolution, you may have to compromise, or digitise (trace over) hardcopy maps. Most of my local mapping involves digitising government online maps.
You could also check what is the Turkey government policies - of the mapping department, and individual departments for specific thematic data (eg agriculture, wildlife, etc).
A growing number of countries have released their main datasets to the public for free. Others will charge you, but not too expensive. And some countries do not want their citizens to have any data, even if we can pay for it.
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I am currently undergoing a research on: The Spatial Analysis of the Incidence and Users' Fear of Crime at Motor Parks in Ibadan.
What is/are the best method for determining the sampling frame or population of study considering the fact that majority of the motor parks in the city have no official capacity or data on daily passenger patronage.
Also, what is the best sampling method to use for administration of questionnaire on passenger in achieving the aim and objectives of the study.
Interesting study there Raji.......
I think the best method for determining the sampling frame or population of study would be to conduct a census of the motor parks in Ibadan, including the number of passengers that visit each park daily. This could be done through direct observation or asking the park owners for their estimates. The best sampling method to use for administration of the questionnaire would be a stratified random sampling approach, which involves dividing the motor parks into different strata based on the size of the park, the daily passenger patronage, etc. The sample size should be large enough to ensure a representative sample of the population.
Thanks.
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Both Ripley's k-function and Moran's index measure the statistically significant clustering within data. However, how to know, which method is performing better for our data?
What are the advantages and disadvantages of each method which can help to choose a better method?
Maybe of minor concern, but let me note that it is kind of misleading to call Ripley's K and Moran's I methods of 'cluster analysis'. I know that ArcGIS does so, but still. Traditionally, 'cluster analysis' means making groups of initially ungrouped observations within a sample. In contrast, Ripley's K and Moran's I analyse the spatial structure of the sample. They do not create groups, but describe/test aggregation of a point pattern or spatial autocorrelation of a variable.
Ripley's K and Moran's I serve different purposes. Ripley's K describes the expected number of neighbours of any point in a point pattern across different radii. You can use it to analyse point patterns in the space, whether they are aggregated, random, or systematic. The only information you use is the coordinates (or distances between them). Moran's I analyses how a measured variable is structured in space. It uses a spatial neighbourhood matrix between the observations and a measured variable for each observation. It has a global and a local variant; with the latter, you can step across different distance classes to reveal scale-dependency in the spatial correlation. If reasonable null-hypotheses are provided, both can be used for hypothesis testing.
Anyway, it would be interesting to outline a real clustering method based on the Ripley's K or Moran's I. Maybe such a method exists already.
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Trying to investigate whether the Autoregressive behaviour is as a result of Lags (SER) or errors (SEM).
Diagnistic check on the weights matrix
The output is as below
spatdiag, weights(W)
Diagnostic tests for spatial dependence in OLS regression
Fitted model
------------------------------------------------------------
logHPRICE = SPOOL + BQUARTER + WFENCE + PSIZE + NROOMS
------------------------------------------------------------
Weights matrix
------------------------------------------------------------
Name: W
Type: Distance-based (inverse distance)
Distance band: 0.0 < d <= 10.0
Row-standardized: Yes
------------------------------------------------------------
Diagnostics
------------------------------------------------------------
Test | Statistic df p-value
-------------------------------+----------------------------
Spatial error: |
Moran's I | -11.468 1 2.000
Lagrange multiplier | 55.698 1 0.000
Robust Lagrange multiplier | 55.302 1 0.000
|
Spatial lag: |
Lagrange multiplier | 2.544 1 0.111
Robust Lagrange multiplier | 2.148 1 0.143
------------------------------------------------------------
The spatial error model is significant and the lag is not. This means you should run a spatial error model
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Hello, I performed a spatio-temporal regression kriging (ordinary) on the residuals from a regression. I would like to know if the ST kriging predictor is an exact interpolator : That is, the values predicted at the sample data locations are equal to the observed value at the sample locations?
Lucas
No, the ordinary spatio-temporal kriging predictor is not an exact interpolator. This is because the kriging model is based on the assumption that the sample data points are not perfectly representative of the underlying trend of the data. Thus, kriging will provide an estimate of the underlying trend, rather than an exact interpolation of the sample data points.
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At the continental level, what did the spatial footprint of African trade routes look like before colonialisation?
Not only did pre-colonial trade occur but some manufacturing also took place and so traders engaged in the sale of manufactured products. Ancient Africa traded in tobacco, gold, copper, spices, ebony, ivory, and skins.
(citation from source).
————
People in pre-colonial Africa were engaged in hunting and gathering, agriculture, mining and simple manufacturing. Agriculture involved most people, so the chapter looks mainly at farming activities. The chapter explains that farmers in those days faced two big challenges: a hostile environment and scarcity of labour.
———————-—
The very earliest evidence of African trade is described by Herodotus (c. 484-425BC) who wrote of the trade across the Sahara; a trade recorded in rock paintings dating from 10,000BC.
————
In most parts of Africa before 1500, societies had become highly developed in terms of their own histories. They often had complex systems of participatory government, or were established powerful states that covered large territories and had extensive regional and international links.
The Transatlantic Slave trade not only distorted Africa’s economic development it also distorted views of the history and importance of the African continent itself. It is only in the last fifty years that it has been possible to redress this distortion and to begin to re-establish Africa’s rightful place in world history.
The African continent is now recognised as the birthplace of humanity and the cradle of civilization. We still marvel at the great achievements of Kemet, or Ancient Egypt, for example, one of the most notable of the early African civilizations, which first developed in the Nile valley over 5000 years ago.
(Citation from:
———-—
The main items traded were gold and salt. The gold mines of West Africa provided great wealth to West African Empires such as Ghana and Mali. Other items that were commonly traded included ivory, kola nuts, cloth, slaves, metal goods, and beads.
——-—
Simple conclusion:
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I would like to use the R package "gstat" for predicting and mapping the distribution of water quality parameters using tow methods (using kriging and co-kriging)
I need a guide to codes or resources to do this
Azzeddine
So you want the code for Kriging using gstat. A simple Google search shows plenty of tutorials. You just need to apply the principles showed on these tutorials to your data.
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Hi, everyone. :)
Language maintenance and language shifting is an interesting topic. Talking about Indonesia, our linguists note that until 2022 Indonesia has 718 languages. Indonesia really cares about the existing languages.
One thing that is interesting, language maintenance and language shift are also influenced by geographical conditions.
To accommodate 718 different languages, Indonesia has a geographical condition of islands. If we move from island to island in Indonesia, the use of the language is very contrasting, there is contact of different languages ​​between us.
Some literature states that language maintenance and language shift are strongly influenced by the concentration of speakers in an area.
So, in the developments related to the topic of language maintenance and language shift regarding geographical conditions, to what extent have linguists made new breakthroughs in this issue?
I think that the study of language maintenance and language shifts related to regions is the same as the study of food availability or state territory which makes the area the main factor for this defense.
I throw this question at all linguists, do you have a new point of view in the keywords language, maintenance, and geographical.
Kind regards :)
Language maintenance is the maintenance of a language (usually L1) despite the influence of external sociolinguistic forces (usually powerful language(s) and language shift, is a shift, transfer, replacement or language assimilation of usually L1 to L2 due mainly to the external sociolinguistic forces influencing a speech community to shift to a different language over time. This happens because speakers may perceived the new language as prestigious, stabilized, standardized over their L1 (lower-status). An example is the shift from first languages to second language(s) such as the English language.
Solution for language maintenance and protection from language shift rests on Social networks.
Social network deals with the relationships contracted with others, with the community structures and properties entailed in these relationships (Milroy, 1978,1980 &1987)
· It views social networks as a means of capturing the dynamics underlying speakers’ interactional behaviours and cultures.
The fundamental assumption is that people create their communities with meaningful framework in attaining stronger relationship for solving the problems of daily life.
Personal communities are constituted by interpersonal ties of different types, strengths, and structural relationships between links (varying in nature) but a stronger link can become the anchor to the network.
For close-knit network with strong ties
Such networks have the following characteristics, they are
• Relatively dense = everyone would know everyone else (developing a common behavior and culture)
• Multiplex = the actors would know one another in a range of capacities
Where do we find some close-knit networks? In smaller communities, but also in cities, because of cultural and economical diversity, e.g. newer emigrants communities, or High-educated individuals.
Functions:
1. Protect interest of group
2. Maintain and enforce local conventions and norms that are opposed to the mainstream -> lingustic norms, e.g.vernaculars, are maintained via strong ties within close-knit communities.
Network with weak ties
These networks have the following characteristics, they are:
• Casual acquaintances between individuals
• Associated with socially and geographically mobile persons
• They often characterize the relations between groups
Lead to weakening of a close-knit network structure -> these are prone to change, innovations and influence between groups and may lead to language shift/language transfer/language/language replacement.
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What do you consider are the implications of Big Data on urban planning practice?
Glory be to Allah... As time progresses, new developments appear that help people to complete their needs with flexibility and ease.
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Is it proposed to use replicates (and if yes, how many) when doing spatial omics, using the same type of tissues but from different animals within the same phylum?
Sure, costs are relevant, but much more relevant is the clear definition of a statistical model that maps meaningful biological features. This is simply not really clear how to do that in a multidemsional space.
If you found some kind of pattern (however you define it), it is certainly good practice to repeat the entire experiment and analysis at least onece (better twice or trice) to see if the pattern you identified occurs (more or less) robustly. If the observed pattern indicates some biological interpretation, you can go back to biology ind infer the hypothesized effects by new experiments (knock-ins, knock-outs, blocking, competeing, histology, etc.).
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Yet I was using the Geostatistical Analyst tool in ArcGIS to interpolate some data. But I found that when the parameter of the semivariogram is same, disjunctive kriging(DK) and simple kriging(SK) gave the same cross-validation result (also gave the same prediction result). I tried to change the transform method and semivariogram model, but the problem wasn't solved.
I searched many papers and found that a few researchers used both SK and DK. I am not sure whether or not I made some mistakes.
If someone met the same problem or know why this problem occurred, please teach me! Thank you a lot!
Hello, try to change the sector numbers and location in the geostatistic kriging steps
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Hello
The most important question: is the (virogram) used in the (ordinary kriging) Is it the (traditional virogram) or (The Residual Maximum Likelihood Method virogram)?
Does (ordinary kriging OK) include the trend, or does it not depend on the trend?
My greetings
In empirical modeling, kriging is a popular interpolation technique used to approximate the functional relationships between impact variables and system response. The interpolation is based on a statistical analysis of the provided data, and a priori set trend functions are an optional addition. However, only trend functions that are linear with respect to the parameters can currently be employed with kriging. In this article, we provide a Kriging extension for dealing with trend functions with nonlinear parameters.
Kriging is the most commonly used geostatistical approach for spatial interpolation. Kriging techniques rely on a spatial model between observations (defined by a variogram) to predict attribute values at unsampled locations. One of the specificity of kriging methods is that they do not only consider the distance between observations but they also intend to capture the spatial structure in the data by comparing observations separated by specific spatial distances two at a time. The objective is to understand the relationships between observations separated by different lag distances. All this knowledge is accounted for in the variogram. Kriging methods then derive spatial weights for the observations based on this variogram. It must be noted that kriging techniques will preserve the values of the initial samples in the interpolated map.
Kriging methods consider that the process that originated the data can be divided into two major components: a deterministic trend (large scale variations) and an autocorrelated error (the residuals).
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Hi everyone,
I have to identify overlapping polygons, with one of the datasets containing thousands of polygons. I am using the sf package and its st_intersects function, as:
dataframe1 %>%
st_filter(y = dataframe2, .predicate = st_intersects)
which takes about 6sec to compare each polygon of the 1st dataframe, and so, days for my current dataframes.
The only way I have encountered so far to make it possible is to first remove some manually and then split the dataframe to run the intersecting.
Would anyone have an advice on how to make it faster?
thanks a lot!
Dear Juliette,
predicate functions can be parallelized. So if you have, let's say, 16 CPU cores, then the computation time is divided by cca 16. I attached the function that can be used for parallelizing sf functions, like st_intersects().
HTH,
Ákos
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Dear Scholars,
Assume a mobile air pollution monitoring strategy using a network of sensors that move around the city, specifically a network of sensors that quantify PM2.5 at a height of 1.5 meters that lasts about 20 minutes. Clearly, using this strategy, we would lose temporal resolution to gain spatial resolution.
If we would like to perform spatial interpolation to "fill" the empty spaces, what would you recommend? What do you think about it? What would be your approximations?
Regards
The interpolation method to use would really depend on how many locations you have data for. The "go-to" spatial interpolation method would be kriging, but if we have <100 locations with data I would rather use inverse distance interpolation as <100 is not enough points for reliable kriging.
You can do it all in R using the 'sp' or 'sf' packages, or use a GIS.
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Dear Scholars,
Assume a mobile air pollution monitoring strategy using a network of sensors that move around the city, specifically a network of sensors that quantify PM2.5 at a height of 1.5 meters that lasts about 20 minutes. Clearly, using this strategy we would lose temporal resolution to gain spatial resolution.
If we would like to perform spatial interpolation to "fill" the empty spaces, what would you recommend? What do you think about it? What would be your approximations?
Regards
Hi,
If you expect some local variation and then a non-stationary behavior of your data, probably Empirical Bayesian Kriging will be the one. This is assuming you have a lot of data non left-skewed and you can asumme Gaussian distribution.
In any case, I recommend you to carry out a Cross Validation with a study of RMSE and AMSE, as you can see in Pellicone (2019; 10.1080/17445647.2019.1673840) or in Ferreiro-Lera (2022; 10.1080/17445647.2022.2101949).
I hope I have been helpful.
All the best,
Giovanni.
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Hi all,
As a non-statistician, I have a (seemingly) complicated statistical question on my hands that I'm hoping to gather some guidance on.
For background, I am studying the spatial organization of a biological process over time (14 days), in a roughly-spherical structure. Starting with fluorescence images (single plane, no stacks), I generate one curve per experimental day that corresponds to the average intensity of the process as I pass through the structure; this is in the vein of line intensity profiling for immunofluorescence colocalization. I have one curve per day (see attached) and I'm wondering if there are any methods that can be used to compare these curves to check for statistical differences.
Any direction to specific methods or relevant literature is deeply appreciated, thank you!
Cheers,
Matt
Edit to add some additional information: the curves to be analyzed will be averages of curves generated from multiple biological replicates, and therefore will have error associated with them. Across the various time points and conditions, the number of values per curve ranges roughly from 200 -- 1000 (one per pixel).
This is great information. I'll take a look first at MANOVA and then at RBSCIs if indicated. Many thanks!
Best,
Matt
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The article "Ethnographic Knowledge in Urban Planning – Bridging the Gap between the Theories of Knowledge-Based and Communicative Planning”, that was published on November 4th 2021 has serious ethical problems, e.g, plagiarism, authorship and duplication.
Hello, again, Tal Berman
Like Usama Badawy , I hope that they at least gave you credit for your hard work. Otherwise, I would contact the editor of that journal to inform of the act. How did you even detect this problem?
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Are these datasets available through spatial analysis tools like ArcGIS? Are they available in libraries of programming tools like R or Python? Are they available at official websites from the Colombian government? Any reference to the specific links or libraries is highly appreciated.
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How can I extend GWR model to GWRK model? I have obtained the residuals from GWR, but I don't understand how can I add the residual kriging for extending GWR model.
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Hi there,
I am trying to use VariogramST function in R, for spatial-temporal kriging.
The values I am working on, are monthly based; while in the function VariogramST, the predefined "tunit" is hours, days or weeks.
I appreciate if anyone can tell me how to change it to months?
Thanks,
Hamideh
Hello, have you solve this problem yet?
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I have to prepare a spatial map of various soil properties for that I am confused about the semi-variogram is compulsory or not?
Most often yes , unless grid size takes into account the area covered...
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I have some environmental covariates derived from the digital elevation model (slope, gradient, channel network distance, etc.) in raster format.
I want to identify areas of similarity between the covariates and somehow identify the smallest possible size area (or areas) to serve as a reference area.
1. Soil data points will be collected to create predictive models in this reference area.
2. The predictive models developed in the reference areas should fit when extrapolated to regions outside the reference area.
3. Therefore, the covariates will cover this external area.
Erupting volcanoes cause sudden, drastic change in an area, forcing organisms to evolve rapidly to adapt to the new environment. Change in an organism's environment forces the organism to adapt to fit the new environment, eventually causing it to evolve into a new species. They eventually become different species. So that changes in environmental conditions can affect the survival of individual organisms or an entire species. Short-term environmental changes, like droughts, floods, and fires do not give populations time to adapt to the change and force them to move or become extinct. However, short and long term environmental changes Quiz - Quizizz. What is an example of how organisms respond to short term changes in the environment? Animals tend to eat a lot more before the change occurs to have stored energy. Animals will go extinct or die due to the short term change. Therefore, in evolutionary theory, adaptation is the biological mechanism by which organisms adjust to new environments or to changes in their current environment. The idea of natural selection is that traits that can be passed down allow organisms to adapt to the environment better than other organisms of the same species.
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I am following the way how a previous paper (PMID: 30948552) treating their spatial transcriptomic (ST) data. It seems like they combined all expression matrix (not mentioned whether normalized or log transformed) of different conditions, and calculate a gene-gene similarity matrix (by Pearson rather than Spearman), and they finally got some gene modules (clustered by L1 norm and average linkage) with different expression between conditions.
So I have several combination of methods to imitate their workflow.
For expression matrix, I have two choice. The first one is a merged count matrix from different conditions. The second one is a normalized data matrix (default by NormalizeData function in seurat, log((count/total count of spot)*10000+1)). For correlation, I have used spearman or pearson to calculate a correlation matrix.
But, I got stuck.
When I use a count matrix, no matter which correlation method, I get a heatmap with mostly positive value pattern, which looks strange. And for a normalized data matrix (only pearson calculated), I got a heatmap with sparse pattern, which is indescribably strange too.
My questions:
1. Which combinations of data and method should I use?
2. Would this workflow weaken the correlation of the genes since some may have correlations only in specific condition?
3. Whatever you think of my work?
You install the R and Rstudio software and visit this web site:
Heatmap in R: Static and Interactive Visualization - Datanovia
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Dear everyone
I am sorry that I'm not so polite and I didn't make the question clear in the last letter.
I am learning FRAGSTATS, and I have two question here:(1) how to get a user provided points raster or table, (2) is the random points procedure repeatable?
(1) how to get a user provided points raster or table
In FRAGSTATS, the input data of user provided points must one of the grid or table, and the manual example only provide the grid and table file, however doesn’t provide the method to generate grid and table files from a point vector file or a table of contains coordination. I tried rasterized point vector files using the Rasterize (vector to raster) in GDAL module in QGIS, but the cells of generated grid doesn’t match exactly the cells of my land cover raster I want to analysis, like this figure attached.
(2) is the random points procedure repeatable
In FRAGSTATS, there is random points procedure, I wonder is this repeatable? Such as the setseed () function in R, after fixing the random seed, you will get exactly the same random numbers/points every time you run your code and your results are reproducible despite the involved randomness. I wonder is there any settlements in FRAGSTATS to handle this problem?
Thank you and best regards.
Así es el SIG puede usar datos vectoriales y generar polígonos....
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I have prepared these two maps of Electrical Conductivity (EC). I used both IDW and Kriging to prepare them. Which one should I choose? The sampling sites are also pointed in the map.
You can see that the Kirging map is quite strange!
Kriging is better but consumes more computer resources. The Kriging result showed in the question is a failure, I guess the reason was too small radius search for interpolation. Try increasing the radius search and you will get a better result.
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Hello!
So, I'm a R user and currently working on a spatial analysis on panel data. Based on my experience for these past days, I understand that 'spml' function on 'splm' package requires strict condition to actually run the analysis (e.g.: balanced panels and complete cases).
Recently, I tried to run 'plm' function instead with self-defined spatially-lagged dependent variable. Will this actually yield the same result? Is there important unaccounted spatial aspects if I use 'plm' instead?
Looking forward to responses. Thank you for your attention!
There are three main types of `pure` spatial models, namely, SEM (with rho), SAR (with lambda) and SLX. Parameters rho and lambda are defined so in 'splm' package and can be different in other contexts.
If you deal with pure SLX, you indeed use `plm` with additional spatial lagged X variables created with `slag` and relevant spatial matrix.
At the same time, `plm` will not be able to capture SAR and SEM results (and their combinations) effectively.
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Can we draw any statistical and spatial relationship between fine parameters of road dust ( Pd, Zn, Cr, and Ca) and some key air pollutants ( like PM_2.5, CO, NO, CH4, O3, HCHO, BC, NOx, SO2)? Did anyone make any spatial analysis using these both datasets?
I have both datasets and would like to do some statistical and spatial analyses. Kindly suggest, if there is any possibility to draw any relationship.
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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 assess economic conditions of each local area. I have demographic data at the city and town levels. I did some research in the small area estimation field, but I couldn't find the exact method which proposes solution for my research question given above. Can you please recommend any book, article, or method on this case? Thank you very much.
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An inquiry about Interpolation Model Validation.
I ran the interpolation of IDW, Ordinary kriging, and EBK. But the R(sq.) values for the all these models (including the semivariograms for OK) rarely exceed 0.1 (even sometimes 0.007 is the highest).
Is model with R(sq.) value of 0.007 good for publication? I think this value indicates too poor prediction, but none of these models is showing a decent R(sq.) value.
On the other hand, the RMSSE is really close to 1, and mean error is around 0.009.
What should I do know?
What can be the possible reason? Am I missing something? Or I should follow more complex models? Is the spatial distribution being controlled more by extrinsic factors (e.g. human interference)?
[120 samples were collected randomly from this study area].
I would gladly appreciate any suggestion.
Just a suggestion, overlay your data with topography (contours or elevation) and soil type, and perhaps land cover type as forest, grassland, and sometimes even vegetation types might produce some differences, such as pine vs deciduous forest, or areas of study that were severely burned, or severely eroded in past land uses, etc. The ground based history and conditions and spatial differences may help explain some of the differences found.
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I am running Geographically Weighted Regression (GWR) on a categorical dependent variable so my model is basically a Geographically Weighted Logistic Regression.
I have multiple independent variables, some numerical and some categorical.
While interpreting the results of numerical variables is straight forward, I want to know how to distinguish the reference level of the categorical independent variables and how to interpret those?
let's say I code males as 0 and females as 1. so the coefficients should be interpreted for females as they are coded 1? what if I coded males as1 and females as 2?
Dear Nasser,
There should be no difference in the results if you coded (0,1) or (1,2)
In both cases, the result will be as a ratio between them. The parameter B equals the ratio of males/females (as males variable is coded with the smaller value)
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I am looking at spatial correlation patterns between grid cells with increasing distances to each other (please see the attached figure 1). I have divided the distance into 11 bins, and in each bin, I have about 150 Pearson correlation values. The violin plot shows the distribution of correlation values within each bin.
Now I would like to estimate up to which distance we can consider a significant correlation?
In other words, how to identify the correlation below e.g. 0.2 is not significant?
For example, in this case, is it statistically meaningful to do something like e.g. calculate the 2-sigma for each bin, and then if we assume the correlation below zero is not significant, and in case the 2-sigma exceeds that threshold we remove that bin (distance) (figure 2).
Or another example, in this case, does the t-test for Pearson correlation coefficient applicable?
thanks for your reply. maybe the term "valid" or "significant" is too early to use it in this stage. Ok, let's go the other way around. Simply, I would like to compare these several datasets (correlation values) (maybe the mean value of each violin box - red line), here you see only one dataset. Should I calculate the rmsd until 2000-km? e.g. why not until 1000-km or 1600-km? because the outcome would be different. I just would like to define a distance range, and justify it statistically.
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Despite going through available literature and getting a basic understanding of how Kernel Density Estimation works when analysing home ranges, I cannot make my Biotas works. Does anybody know about some more detailed guidelines/manuals/training videos etc., for Biotas? Provided manual on their website says very little about navigating within the software and setting required parameters. For instance, choosing a window with (LSCV), I have no idea what units are represented there. In my case, I have a limited dataset, let's say less than 100 GPS fixes of the Little owl, which moves on a small scale. Moreover, it roosts often on the same spots; therefore, my points are dense and sometimes even overlaid. I would like to avoid manipulating hard data and exclude those overlaying points from analyses because I still think they have important information value. Would it be still possible to make a fixed KDE with LSCV under such conditions?
Any help will be highly appreciated. Thx a lot.
I'm not familiar at all with the process in Biotas, but using ArcGIS and QGIS the process is fairly straight forward using the tools and plugins in each. Without knowing anything about your project, it would seem to me that throwing out location data would diminish the accuracy of the KDE and should only be done if you are reducing the sample rate over the entire dataset and not just location or specific instances of a location as it would skew the results of the actual densities.
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If i want to calculate say global morans' I-index over the observation that is dispersed in administrative units to measure the level of clustering on a certain variable is it relevant to do it if i use the geographical data on the administrative units? So observations within the same administrative unit will have exactly the same position and I don't want to measure clustering between the observation across administrative units (i.e I set the observations to be neighbours within each administrative unit). Is a spatial approach the correct way to approach this question if i want to examine if observations in admin unit A have higher levels of the measured varaible in question and are thus clustered compared to admin unit B that has more of a mix of levels of the variable?
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I try to calculate Stream Power Index (SPI) in ArcGIS. For this reason, i checked many videos or documents but there is no certainty about the formula in the raster calculator. So I wrote these formulas below to learn which one is right. Each one creates different results.
DEM Cell Size=10m
SPI_1 --> "flow_accumulation" * 10 * Tan("slope_degree" * 0.017453)
SPI_2 --> Ln("flow_accumulation" * 10 * Tan("slope_degree" * 0.017453))
SPI_3 --> Ln("flow_accumulation" + 0.001) * (("slope_percent" / 100) + 0.001)
SPI_4 --> Ln(((flow_accumulation+1)*10) * Tan("slope_degree"))
SPI_5 --> "flow_accumulation" * Tan("slope_degree")
Also, while creating a slope, am I need to choose which one? DEGREE or PERCENT_RISE
And the last question: When I calculate SPI with the formulas above, I get SPI map that included negative values. Is it true? Are negative values a problem or not?
i think the correct formula is SPI_2 where:
flow accumulation must be in square meters and then not multiplied for 10;
the slope degree must not be equal to 0, so add + 0.1 degrees
In this way, SPI should be a dimensionless positive value (more or less)
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Which model is better for investigating outcome – exposure relationship spatially?
· Data are counties
· Dependent variable is incidence per county
· Independent variable is median of XXX per county
· Spearman’s correlation, significant and negative
· Spatial autorelation of incidence values close to zero
· Local clusters, detected but majorities are single counties
· Geographically weighted regression, local coefficients mix of positive and negative but global regression coefficient negative
With this background, we should go for geographically weighted regression or bayesian convolution model with structured and unstructured random effects.
I am not convinced that countries are in general an appropriate level for measuring exposure due to large within country variations. I would need to know the context in which you are doing this work.
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We think to test this hypothesis in Morocco within our research project, but we wonder if the error in the absolute altitude could prevent from using these data to this goal.
Dear Linh
are you interested in using radar data for soil erosion survey?
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I have a point shapefile of 12 islands (created by using centroid in Arcmap) with attribute of Macro benthic speciesl and their abundance. I would like to analyze a distribution of these species using Arcmap. Would it be suitable to use kernel density to analyze Species distribution? If not, which method should be used in analyzing instead?
I am concerned about number of points (too few points) and long distance among island.
Thank you
maxent software
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I am computing taylor series expansion in which distance of a objects from a origin(0,0) is computed. I am expressing the distance in terms of sum of position and velocity*time of the object. I only wrote the first two terms of taylor series expansion.
Please check if the expansion in the document attached is correct.
The two term Taylor expansion in equation (2) is not clear.
Would you clearly specify the distance function which you have considered in the expansion. Also clearly state the dependent and independent variables.
In fact, it is not a big deal; several calculus books teach Taylor expansion of functions of one, two, three...variables. But why is Taylor series expansion for?
Thanks!
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Fitted values: prediction on the dataset used for fitting the model pred1.m0 <- predict(m0, newdata = wheatdata) pred1.m0[1:5,]
Genotype prediction
pred2.fit.SpATS <- predict(m0, which = "geno")
pred2.fit.SpATS[1:5,]
above mention two commands output file is BLUEs or BLUPs value?
BLUE
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I am studying the effect of the land-use surrounding a location on the abundance of aphids on this location. To do this I fit a linear model with the land-use as independent variable and the abundance of aphids as the dependent variable. To check for spatial autocorrelation I plot the correlogram with the Moran I of the model residuals in function of the lag distance.
However I have multiple years of data: where the aphids have been observed each year together with the surrounding land-use. How can I account for this temporal effect? Should I incorporate a 'Year' variable in the linear model and can I then just look at the correlogram of the whole dataset?
Hi,
There are so many ways, yet I would prefer R (Rstudio) software for this.
More methods could be found here: -
doi: 10.1111/j.2007.0906-7590.05171.x
Cheers,
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I am working on a project on the spatial variability of Soil EC and i am a bit puzzled on the comprehensive steps for kriging regression and semivariogram output on QGIS.
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I am studying walking behavior by agent based modeling. Do you see proper Anylogic software on it? Or another software?
Also, do you know any sample in which analyzed walking behavior by Anylogic?
Best Regards,
Luis Cruz
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Hi everyone!
I have origin points in different areas and within these areas also specific values within raster cells (in 50m resolution). My goal is to sum up the values of all previous cells in the respective cells that are traversed.
This movement can be calculated for example with Least Cost Path. With this tool i created a Backlink Raster, which shows me the movement.
But when I use the Least Cost Path tool to accumulate values, the respective values are extended by the grid cell size.
Does anyone have an idea how this can be used to accumulate only the actual values without the grid size?
I tried this with flow accumulation, but some cells will get no value, this i because there is no other cell ending in it. But it needs the value added from the prior cell (or value of the cell) in which this cell is "moving"/"flowing".
I hope someone could help me out with this issue.
Cheers!
A movement is not uniform. The direction is constantly changing, so a summation of rows or columns does not make sense. Also the movement starts somewhere in the middle of an area.
I will attach an example.
So if you follow the arrows backwards, in the last cell there should be the sum of all values which are passed through.
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I have attached the OSM map of Pannipitiya, Sri Lanka. So looking at the map, what kind of geographical questions you can ask?
To me, the following came to my mind
1. What are the places where house dwellers can walk and reach within 1 minute (600 m ?) ?
2. What is the calmest and quiet place to meditation?
Describe the geographical features and locations of the places and roads on the map, in relation to one another
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Drear All
Hoping you are doing great,
I’m conducting spatial analysis on Expressway in Malaysia, however, I don’t know how to determine the length of the hotspots because the clustering of hot spots using Get G* method with fixed bandwidth 1000 m, sometimes hotspots come in one isolated location from the others on the network as on the attached image.
So, you may someone can explain to me how I can measure and determine the length of hotspots?
--
Thank you
Best Regards
Fathi Salam Mohammed Alkhatni
There are also ways to get an metrics using raster neighborhood operations.
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As a part of my PhD, I conducted a study to assess health inequities in Amaravati capital region of Andhra Pradesh using two composite indices made from health determinants indicators and health outcome indicators.
Health outcome indicators data was available at the sub-district level. The data were interpolated to create a heatmap of the health outcome index. Whereas health determinants data was available at the village level. Thus I created a choropleth map using the health determinants index.
Later interpolated health outcome index map was overlayered on the choropleth map of health outcomes. It highlighted some interesting findings, i.e. areas of concern (Villages). The colour combinations created because of overlaying two layers revealed the areas with poor health outcomes and poor health determinants and areas with poor health outcomes with better determinants.
Kindly check these files and give your valuable opinions. Whether this type of analysis can be used to highlight the areas with health inequities or not? Please comment on the method used and the results obtained in the overlayered map.
The OPGD model and "GD" R software package were recommended to identify spatial determinants from a perspective of spatial heterogeneity. You can refer the guide to use the model https://cran.r-project.org/web/packages/GD/vignettes/GD.html. As a result, you can visualise contributions of determinants, and the interactive impacts of spatial variables.
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I have 39 datasets of georeferenced disease severity data for which I would like to conduct a spatial analysis. As a part of this analysis, I would like to compare the amount of spatial autocorrelation present in each dataset.
For disease incidence data (count-based data), there is the SADIE procedure, which is widely used for this kind of task. In contrast, for disease severity data (continuous data), I am not aware of a statistic that can be or is used in that kind of way. The most popular statistic, Moran’s I, seems to be solely used in an inferential kind of way (presence or absence of spatial autocorrelation).
I am aware that the spatial weights matrix used for calculation of Moran’s I complicates the comparison between datasets. But, given a somewhat constant spatial weights matrix between datasets (for example Inverse Distance Weighted?), wouldn’t it be possible to compare the results? In addition, this GeoDa video https://www.youtube.com/watch?v=_J_bmWmOF3I seems to indicate that a comparison based on standardized z-values is in principle possible. Nevertheless, I am not aware of a published study in which this kind of analysis was carried out.
Therefore I would like to ask: Does anyone know of such studies? Or maybe of another statistic that would be better suited for this kind of purpose?
Any suggestions would be greatly appreciated.
Regards,
Marco
Dear Marco,
That test will give you a coefficient (-1 to +1), as you aware of it. So, there is no need to a special test. You can compare them. Also, for this matter you can take Z-scores into account.
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Hi, I managed to estimate SEM, SAR and SDM for spatial panel models. However, to test whether the SDM can be simplified to SAR or SEM, there is a need to conduct Wald or LR test. I am thankful if anyone can share the command/instruction of Wald test and LR test for spatial panels using STATA. Thanks.
Based on spregxt, I obtained results as shown below. The results of the LR and Wald testing on panel groupwise heteroscedasticy. Are these results related to reduction of SDM to SAR/SEM? I feel uncertain about this, pls advise. Thanks.
* Panel Groupwise Heteroscedasticity Tests ============================================================================== Ho: Panel Homoscedasticity - Ha: Panel Groupwise Heteroscedasticity - Lagrange Multiplier LM Test = 7.4773 P-Value > Chi2(6) 0.2618 - Likelihood Ratio LR Test = 6.2353 P-Value > Chi2(6) 0.4190 - Wald Test = 11.5822 P-Value > Chi2(7) 0.0958
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I have NYC taxi trips dataset contains multiple attributes like (pickups and dropoffs) coordinates, DateTime also, trip distance and so on, indexed by "tpep_pickup_datetime" coulmn as datetime64[ns] data type, I extracted some features from date time pickup and drop-off columns like month, day, hour and other. So I am focusing on DateTime and location to do trip time prediction.
Concerning the problem that faced me, it is the problem of converting the dataset into a fixed time series intervals (1 or 10 mins as an example to get 1440 time bins for each day) to be ready to LSTM input, let me reflect the essential point of the problem, I have tried to do a resample the dataset based on pickups-time, but the dataset contains more features, so it is difficult to convert it into a sequence time series with fixed interval lengths. Because the data contain a lot of trips at the same time (such as Sunday at 8:00-9:00 am approximate 2990 trip) but they are from different places.
So the main problem which is briefly: "How do I convert or prepare a taxi dataset to a time series with the fixed intervals?".
There is one publication focus on taxi trips of NYC :)
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I am a novice researcher, and i'm working on a project which is busy analyzing water quality data from different water sources such as dams,rivers, and springs , and also from secondary sources such water treatment plants, and households, i have collected water GPS coordinates, which appear on the image* , hence im having difficult to find the right methodology to analyse these results spatially. The microbiological parameters that are going to analysed include bacteria such as E.coli, salmonella spp , Shigella spp, Giardia spp, Entamoba Histolytica spp.
i have attached an image showing the sample locations , additionally the study area is in six Quaternary drainage basins
Use principal component analysis (PCA).
Look at this paper.
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Dear All , I am biggener in Bayesian spatial modeling . I have structured and unstructured random effects in my formula . I extracted and mapped the posterior means . I can't understand what a high/low value of structured and unstructured means (interpretation).
R code is as under:
nb <- poly2nb(map)
map\$re_u <- 1:nrow(map)
map\$re_v <- 1:nrow(map)
hyperprior2 <- list(theta=list(prior='loggamma',param=c(1, 0.0005)))
formula2 <- obs ~ NDVI + f(re_u, model = "besag", graph = g, scale.model = TRUE, hyper = hyperprior2) + f(re_v, model = "iid",hyper = hyperprior2)
res2 <- inla(formula2, family = "poisson", data = map, E = E, control.predictor = list(compute = TRUE),control.compute = list(dic = TRUE))
summary(res2)
map\$RR <- res2\$summary.fitted.values[, "mean"]
map\$LL <- res2\$summary.fitted.values[, "0.025quant"]
map\$UL <- res2\$summary.fitted.values[, "0.975quant"]
map\$spatial <- res2\$summary.random\$re_v\$mean
map\$nonspatial <- res2\$summary.random\$re_u\$mean
tmap_mode("view")
tm_shape(map)+ tm_polygons("RR", alpha = .50)
tm_shape(map)+ tm_polygons("spatial", alpha = .50)
tm_shape(map)+ tm_polygons("nonspatial", alpha = .50)
These blogs might be useful, have a look:
Kind Regards
Qamar Ul Islam
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Hello dear researchers, is there any source (except Bangladesh Meteorological Department) to get the daily meteorological data (air quality, rainfall, temperature etc.) of the BMD stations of Bangladesh for the last 10 years for free?
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I am not a spatial analyst or an expert in a related field, that's why I decided to contact you for advice or help in extracting maximum information from the data I have.
Data (2 trials) - routes crossing the study area (about 1250 sq. km.), about 1000 points along the lines represents an event of species occurence, and a collumn about the number of occurences (z data) attached to the x,y coordinates.
Questions:
Which Exploratory Data Analysis tools I should try to describe the pattern? I have an idea about visualising the Standard Distance circle (Centrography), box plot of an events, for example. Maybe Kernel Density? And what I can do with z data? Maybe interpolation? I think It would be easier to work with transects, but I don't know what I can do with this kind of irregularly shaped data. Can I do some tests or predictions with this kind of data, or the samples are too small and not representative? For example I have a hypothesis that the distribution of an event is not random, and the binomial probability and number (z) of events is higher in the central south part because of some factors.
I look forward to your suggestions on what tools, tests I can use, which concepts I should learn about.
Now I am trying to use R and QGis for visualisation and analysis.
I also apologize that my english may confuse you.
Gabriel Asato Thank you! I already visualised the data using heat map tool on Qgis, but it rightly just shows the more surveyed areas (bigger line length per square unit) as denser
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Hi everyone,
At the moment I am designing a spatial gene expression experiment using the 10X Visium assay. There are a few papers out there that have used this assay. There are also several packages available to analyze the data (e.g. Seurat). However, if I am correct, none of these methods take biological replicates into account. In other words, is it possible to align different slices of biological replicates and then perform differential expression analysis to compare conditions?
Honestly, this tech is still in its infancy, and is also _suuuuuper_ expensive. Most people are hard-pressed to make use of the gigabytes of sequence data they get from a single experiment, let alone take that forward to N=3 or more.
That isn't to say what you're proposing is impossible, or even impractical, but more to suggest that for such specific questions there are usually cheaper, more specific solutions. You absolutely can compare multiple RNAseq datasets, and adding a spatial component to this should also be possible provided your segmentation/tissue designation is solid enough (or you have cell-specific markers to aid comparisons), but really...I think you need to carefully formulate your question and work out exactly what this very, very expensive (but very, very neat) approach can do that other more conventional methods cannot.
Also, obviously I am jealous of your budget and resources, but that kinda goes without saying. ;-)
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