Spatial Analysis - Science topic

Thank you Ma'Mon Abu Hammad

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?

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.

Chandan Roy

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:

Javid Hojabri

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.

Dear Usama Badawy,

I appreciate the time and effort you have taken to share your knowledge with me.

Gerard Taulé Codinach

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:

Hazrin Hasim Thank you for your reply. I will explore the packages you cite.

Terima Kasih you for your take, Hazrin.

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:

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.

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.

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.

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.

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.

The spatial error model is significant and the lag is not. This means you should run a spatial error model

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.

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.

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.

(citation from: https://www.aehnetwork.org/wp-content/uploads/2016/01/Green.Production-Systems-in-Pre-Colonial-Africa.pdf

———————-—

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.

(Citation from: https://african.business/2021/10/trade-investment/a-short-history-of-african-trade/

————

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.

(Citation from: https://www.ducksters.com/history/africa/trade_routes_of_ancient_africa.php

——-—

Simple conclusion:

Colonization brought a full disruption of Africa‘s traditional trade and routes.

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.

I would advise you to start with Kriging and then move to co-Kriging.

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.

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.

Such networks have the following characteristics, they are

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:

These networks have the following characteristics, they are:

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.

Glory be to Allah... As time progresses, new developments appear that help people to complete their needs with flexibility and ease.

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.).

Hello, try to change the sector numbers and location in the geostatistic kriging steps

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).

For further reading and answer to your question, please click on this link

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

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.

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.

Hi Mairtin Mac Siurtain, thanks so much for your reply!

This is great information. I'll take a look first at MANOVA and then at RBSCIs if indicated. Many thanks!

Best,

Matt

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?

Younis Ahmed These links might help:

1. https://geonode.wfp.org/layers/geonode:col_first_level_admin_boundaries/metadata_detail

2. https://data.humdata.org/dataset/cod-ab-col

Hello, sure, the GWR tool in ArcMap calculates the residuals (https://desktop.arcgis.com/en/arcmap/10.5/tools/spatial-statistics-toolbox/geographically-weighted-regression.htm#GUID-E9FD5D8B-103B-40FC-93DD-E84BB2F1C7E9), and you can use this for kriging.

Hello, have you solve this problem yet?

Most often yes , unless grid size takes into account the area covered...

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.

You install the R and Rstudio software and visit this web site:

Heatmap in R: Static and Interactive Visualization - Datanovia

Así es el SIG puede usar datos vectoriales y generar polígonos....

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.

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.

Check https://www.mdpi.com/2073-4433/12/9/1109/pdf

Furkan Oral

These articles might be an asset, have a look:

1. https://www.adb.org/sites/default/files/publication/609476/small-area-estimation-guide-nsos.pdf

2. https://ec.europa.eu/eurostat/cros/system/files/Lesson1_SAE%20EMOSschool_last.pdf

3. https://unstats.un.org/sdgs/files/meetings/sdg-inter-workshop-jan-2019/Session%207.c.1_Country%20Presentation%20Thailand.pdf

4.

5. http://doc.ukdataservice.ac.uk/doc/6792/mrdoc/pdf/6792_small_area_estimation_using_esds_government_surveys.pdf

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.

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)

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.

Tomáš Bušina,

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.

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)

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.

Dear Linh

are you interested in using radar data for soil erosion survey?

maxent software

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!

BLUE

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,

In addition, the video link provided would also do much explanation to your question

Good question, I'm trying to follow the same issue. Maybe you can have a look about this interested courses:

Best Regards,

Luis Cruz

thanks again for your reply.

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.

Describe the geographical features and locations of the places and roads on the map, in relation to one another

There are also ways to get an metrics using raster neighborhood operations.

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.

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.

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

There is one publication focus on taxi trips of NYC :)

Use principal component analysis (PCA).

Look at this paper.

https://www.mdpi.com/1660-4601/17/8/2942/pdf#:~:text=Principal%20component%20analysis%20(PCA)%20was,the%20sources%20of%20water%20pollution.&text=It%20also%20showed%20that%20agricultural,pollution%20in%20the%20study%20area.

Dear Muhammad Alfahad

These blogs might be useful, have a look:

1. https://www.datamation.com/big-data/structured-vs-unstructured-data/

2. https://www.xplenty.com/blog/structured-vs-unstructured-data-key-differences/

3. https://www.formpl.us/blog/structured-unstructured-interview

4. https://www.geeksforgeeks.org/difference-between-structured-semi-structured-and-unstructured-data/

Kind Regards

See the link

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

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. ;-)

Hey, Iraida Redondo García sorry for the delay.

I think that the unit will depend on which unit is the coordinate, no? If it's decimal degrees, then the distance is in decimal degrees.