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.
The easiest procedure to make an EDA for this case is to display a density of ocurrences map along each route by display the points over each line. There are a fast and non analytical tool in QGIS for perfoming such task. layer/properties/symbol/heat map.
I have other ideas like try to get some stats from each path but I am not sure what do you want to do, or what kind of data is useful for you.
I would gridify it and use that grid to make some kind of kernel based calculation with distance as weight.. https://rdrr.io/cran/marmap/man/griddify.html . Kernel density analysis available in qgis and gridify is available in R... just some thoughts..
What kind of species you are talking about? Is this related to environmental studies like habitat? If your problem is the irregularity of data, you can assign weights, which usually are related to environmental factors like water proximity, food, topography, and land cover/use.
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
Rengifo Ortega Thnk you! I tried to gridify my dataset, but I get the following error:
Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘extent’ for signature ‘"Character"’
John Hatzopoulos, yes, this data is from environmental inspection, but I want to extract more information about species distribution. Yes, I wanna add some weights to it, and want to measure, is there any relation to the water proximity
Water proximity has to do with the highway crossing to get water and return to the place considered as the home place.
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