Sounds like a use case for Trajectools -
Day trajectories from point layer. It creates lines from timestamped points, e.g. here is an example of three days of ship movement:
Computing the length per line can then be done as described by Gabriel.
While the tool should be easy to use, the installation is a bit involved on Windows since the plugin requires ...
Lets look closely at the error, and, in conjunction with some knowledge on how R works, figure out what is actually happening, and how to debug it:
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘shapefile’ for signature ‘"NULL"’
If you read the help for shapefile:
## S4 method for signature '...
This will give you a rectangle of 20 x 50m at one side of the survey point, which is oriented to the next survey.
project($geometry, -25, azimuth(
First, you need a field populated with the day, to later group the waypoints by day.
In your case, you can create a new string type field and populate it in the field calculator with the expression:
substr( "name", 6, 7)
It will extract a substring of the "name" field, starting in the 6th character, and with a length of 7 characters (i.e., 21APR16). ...
Yet another approach using the Field Calculator... this will work only if your GPS attribute table is sorted according to the recorded date/time.
A dummy GPS point data crossing Road (I've got road 'fid' = only '1').
Now please ope the GPS layer's attribute table and start the Field Calculator.
Give this expression:
Reuben, good day to you. I have solved problems like yours with the QGIS tool PointstoPaths (note the plural Paths). This is an extraordinarily useful tool. Sadly, it is only available at QGIS v. 2.X. If you don't have v. 2.X installed, download the latest version at:
For my Windows 10 machine, I use the install file QGIS-...
Treat your output as temporary and allow it to be replaced each time. Start with an empty master table with the same fields as your outputs, ready to receive your output and use Append to copy the temporary output into the master table.
This will give you a table with all of the outputs.
If you are using iteration as stated here (for value = x to y ...
Firstly, you may run a simple code from the Python Console. You only need to preliminarily load the point layer and the polygon layer in the Layers Panel and then typing their name and the field name where the cluster size value is stored:
from PyQt4.QtCore import QVariant
point_layer_name = 'points' # name of the point layer
grid_layer_name = 'grid' # ...
You might consider EpiCollect. I don't have much experience with it, so I cannot strongly recommend it, but it might meet your needs. Some of it's advantages are:
allows you to build customized data entry forms
runs on Android
and it appears you can customize the interface to collect data with a one-to-many relationship.
Hope that helps.
Maxent is supposed to predict probability of presence due to the co-variates you are testing (usually environmental variables). It is a presence only model and is not an abundance model so I really fail to see how the paper you mentioned use number of mosquitoes (the link does not work).
One option is of course duplicating that presence point represented ...
You have a good description of the problem. It is a nice logic question which can be dealt with by gis.
I would create a line feature between all the points. The attributes of each line would contain the average of time and somehow average of the gps readings.
Then intersect the road with your new line features. Potentially the intersection will give you ...
Trying to follow the suggestion by Vince, below is a QGIS expression approach.
We need to find out (1) the center of this ellipse as point O, (2) semi-major axis length x which is the half of the total distance the elephant has moved during the time T (... as you see the distance A -> B' -> B is the same as O -> B' -> O). And (3) the semi-minor ...
Using the finest resolution possible is not neccessarily the right choice when doing habitat modeling. I can't think of any papers to cite right now but consider this:
When creating a habitat model (HM) the resolution highly depends on the species you are looking at. A mouse has a very different moving and cognition range from an elephant. Thus, using a 1x1 ...
The issue you have is that your data covers a very small area, such that you couldn't possibly make any inference about the species response to different climate variables.
Were there any other attributes collected with the species data relating to physical habitat? This could be slope, elevation, soil type, vegetation type etc. You might be able to find ...
Getting temperature estimates at finer resolution than 1 km is not likely to happen without you placing your own temperature loggers around your study site. You're going to have to look into using different explanatory variables. If you hope to be able to determine any drivers of distribution in an area that small, especially for wildlife (vs. plants because ...
This is relatively easy if you store the data as a Point layer in sequential order.
Assume your layer name is bird_move
Create a new field azimuth which represents the azimuth between points.
Create a new field angle (difference between consecutive azimuth values).
Start the Field Calculator and give expressions as below:
Have you tried posting this to the MaxEnt Google group? You might get a better answer there.
Anyway, MaxEnt is for presence only data - I don't recall ever reading a paper where count data was used. What they probably did was something like put several points very close together in order to simulate count data at a single location. For example, if you were ...
One approach is to save the output to the in_memory workspace, which will be much faster than writing to disk. You can use the %n% inline variable to provide a unique name (i.e. the iteration number) for each in_memory object:
You can use the same inline variable to save physical copies to disk so that you are not overwriting output: