Spatial Xl Crack
Fanny Lococo
I have four feature layers that have multiple polygon features within them. I have set up a selector widget for each of these that is hosted on the header. If I choose an individual polygon feature from the selector widget and filter the points (what I am interested in) by spatial location, the widget also returns points that are not within the polygon as can be seen below. The green polygon is Fooses Creek and only points within that polygon should be displayed.
Each of these study areas was supplied by a different agency and I spent 1.5 days making sure that all of the edges align and that there are no overlapping pixels between the polygons within each of the feature layers as well as there not being any overlapping pixels. When I tried those feature layers, I got points filtered from adjacent polygons. When I looked on the discussion board I found an individual that buffered the polygons by -5 meters to ensure that there were gaps between them and therefore the spatial filter would not return adjoining polygon's features (points). The photo above is from this buffered map. See picture below:
Can anyone provide any guidance about what else I should try? This seems like a big limitation of using a Dashboard if you cannot filter points that are completely within a polygon because all the numbers that are returned are not correct. The same goes for using a negative buffer to try and force the spatial filter to work. These areas are no longer exactly correct with the gaps. It seems like this worked fine on the old version of Dashboard, or am I wrong about that?
Thank you for any suggestions. I will stop working on this for the time being and wait for an answer because as I stated above I have worked for three straight days on lining up all the edges and then creating the negative buffers.
Sounds like you didn't click the "Only render when filtered' checkbox. Counterintuitively, it's meant to read to say "Only render those things meeting the filter criterion" (under Setting/layer action/filter).
"Point", "Polygon", and "Line" are the three principal types of SpatialObj fields in Alteryx. If there are multiple objects within that field, then the most complex object will determine the type (If a line and Polygon are in a single object, it will be a polygon). Use the Map Input tool to draw some test objects if you'd like.
I'm not looking to watch videos/lessons or throw tests at tools to figure out how they work. I want Alteryx to tell me exactly how their product functions; expected inputs, expected outputs, formats, etc. I realize I can reverse engineer tools and determine how they work but that is a waste of time and is not 100% reliable.
A useful piece of documentation would say "the Spatial Info tool (ST_ObjectType) can output the following (n) object type strings; Point, Line, Polygon, PolyPolyLine, None, A, B or C" Then explain what each of these outputs represent.
I have made a macro, for general use, that converts spatial object to a particular format based on the Spatial Object Type. I just don't want to have to continue to modify the macro if a new, to me/the process, object type comes along. Thus a definitive list of all possible object types would be handy.
I'm trying to conduct a spatial match on two shape files which both consist of the exact same points. Unfortunately there isn't another attribute that can be used to join the two files together, so the only commonality is the spatial object. I tried there different spatial match options (e.g. where target intersects/contains/within/touches universe, etc.), but it doesn't produce any results. Is there any way to spatially match the exact same points?
Not sure if this works for your use case, but you can set spatial objects equal to each other, which will return a true (-1) or false (0) if they match. Simple workflow showing this with trade areas attached.
fyi - you can use find nearest to match two spatial points which are equal to each other (as long as you allow zero distance matches)... What is your end goal? You can also use the unique tool with spatial points - like to compare borders between states.
Does anyone have any ideas on how to create groups or clusters for spatial points? I have about 1,200 points positioned globally and I need to group the ones that close to each other. The only information in the table is a unique ID and lat/lon coordinates.
I was thinking of creating 20 or 30 mile voronoi rings then combining the spatial trade area to create a single group. Then use Find Nearest to assign points that are within 2 or 3 miles of a trade area. That should help with the stranglers. Problem is in areas where the sites are really dense. Ex NYC. The group can get really large almost cover half the Northeast.
What you could do is to define a grid system, and then any grid with more than a certain density, the definition of close is "within 1 mile", and when the density is lower, then use a far broader tolerance (like for Alaska or northern Europe).
Yes, I did @SeanAdams. Thanks for the help. I ended up running the K-means and then creating a 30 mile buffer around the centroid of the k-means grouping (Client was demanding 30 mile buffers). Locations that fell outside of the 30 minute buffer were assigned their own cluster. Thanks again for the help.
Thank you so much for posting this. I have close to 2 TB of data with Lat and Long information with Timestamp for every second. something like this for eg in the attached format. So when i plot these points on US map i would like to have cluster points which are close to each other with large cluster having bigger size etc..
Base R functions are complemented by contributed packages provided as source packages, and as ready-to-run binary packages for Windows and macOS (Intel 64-bit and Apple Silicon arm64 architectures). Information about source installs of packages using software external to R may be found at the end of this page. This task view covers the current status of contributed packages available from CRAN.
The contributed packages address two broad areas: moving spatial data into and out of R including coordinate transformation, and analysing spatial data in R. Because the contributed packages constitute an evolving ecosystem, there are several points of entry for users looking for help and information. Two informal organisations curate websites: r-spatial with a hyphen, and rspatial without. R-spatial is more generally geo-informatics based, grew from the legacy sp package and is now clearly aligned with the modern sf and stars packages. Rspatial has grown from the raster package, now moving towards the modern terra package. It is also worth noting the wealth of online book projects, which may be helpful for users seeking an introduction, including Geocomputation with R.
Specific questions or issues may be raised where packageDescription()$BugReports returns an URL for bug reports or issues (where is the name of the package as a string), or directly with package maintainers by email. Use may also be made of the R-SIG-Geo mailing-list after subscription, or of Stack Overflow with appropriate tags, or of Stack Exchange. Using the #rspatial tag on Twitter and Mastodon may also be worth trying, or browsing traffic using that tag (among others).
The packages in this view can be roughly structured into the following topics. If you think that some package is missing from the list, please e-mail the maintainer or submit an issue or pull request in the GitHub repository linked above.
Many of the packages for handling and analysing spatial data use shared classes to reduce duplication of effort. Up until 2016, the sp package provided shared classes for spatial vector and raster data, but the representations used preceded more modern and efficient international standards for spatial vector data. From the release of sf, these modern vector representations are to be preferred. For spatial raster data, the representations proposed in stars and terra suit overlapping but slightly different requirements. Conversion between objects of classes defined by sf, stars, terra and the legacy sp packages are available, and are described in Conversions between different spatial classes in R.
Spatial data is most often represented by one of two data models, vector or raster, and both models have many of their own file formats. GDAL (Geospatial Data Abstraction Library) is a (non-R) library that provides a unified way to read and write hundreds of spatial data formats. Formats supported by GDAL include both OGC standard data formats (e.g., GeoPackage) and proprietary formats (e.g., ESRI Shapefile). GDAL is used by a large number of GIS software and also many R packages, such as sf, terra, and vapour. This allows us to read and write spatial data in R from and to various spatial file formats. Important note: CRAN offers binary versions of packages sf, terra, and vapour for Windows and macOS, that contain specific GDAL version with a subset of possible data source drivers. If other drivers are needed, you need to either use other conversion utilities or install these packages from the source against a version of GDAL with the required drivers.
In the past, rgdal and raster (through rgdal) were recommended for reading and writing of spatial data in R. However, due to the retirement of rgdal on 16 October 2023 new projects should not use it, and existing projects should implement migration to the packages mentioned in the previous paragraph. In addition, rgeos and maptools were archived at the same time. Further details and links may be found in project reports on the evolution project. From October 2023, sp only uses methods from sf in place of those from rgdal for projection and access to the underlying definitions of coordinate reference systems.
The division of spatial statistics into three partly overlapping areas: point pattern analysis, geostatistics and the analysis of areal/lattice data, is widely accepted. However, areal data analysis can be split into disease mapping and spatial regression (also partly overlapping). In addition, ecological analyses often approach spatial data in particular ways, giving rise to a specific topical cluster of packages. All of these approaches to analysing spatial data treat the spatial relationships between observations as a way of exploring and making use of important sources of information about the observations over and above what is known when assuming that they are independent of each other.
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