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15

The idea with hexagons is to reduce sampling bias from edge effects of the grid shape, which is related to high perimeter:area ratios. A circle is the lowest ratio, but cannot form a continuous grid, and hexagons are the closest shape to a circle that can still form a grid. Also, if you are working over a larger area, a square grid will suffer more from ...


12

One of the benefits, that I've seen when doing wildlife or habitat modelling especially, is that hexagons allow patterns in the data (ex, edge of a field or any other patch) to be seen more easily than what squares would of offered. Think of a soccer ball too, though not always hexagons, those geometric shapes fit to a curved surface quite nicely. In your ...


7

The link provided by @Mapperz mentioned that some of the potential sites "were considered at a MSL Project and MSL Landing Site Steering Committee meeting in Dec. 2009 that emphasized discussion of the science merit of the sites as well as landing site safety based on initial evaluation of thermal inertia, slopes, and other first order safety parameters." ...


7

The hexagon is the most complex regular polygon that can fill a plane (without gaps or overlap). I can see two advantages: It is closer to a circle than the square in terms of shape index, so you suffer less from orientation bias. The "length of contact" is the same on each side (with a square, the neighbours include the four squares at the corners). ...


6

I believe the AreaOnAreaOverlayer is the transformer that performs the equivalent of an ArcGIS Union. Performs an area-on-area overlay so that all input areas are intersected against each other and resultant area features are created and output. The resultant areas have all the attributes of all the original features in which they are contained.


6

This paper: Christophe & Ruas 2002, Detecting Building Alignments for Generalisation Purposes, ISPRS, Ottawa. describes an operational method for the detection of small surfaces (buildings) alignements - it should work even better with points! (this method is rather robust since it is used for the production of 1:25000 maps in France).


5

I found the solution! arcpy.Describe object can easily handle this. Here is the example same for feature and raster: buildings is a Shapefile layer, dem is a TIFF layer and added in ArcMap feature = arcpy.Describe("buildings") print feature.extension output message : shp raster = arcpy.Describe("dem") print raster.extension output message : tif


5

Use os.path.splitext() import os filename, ext = os.path.splitext(r'C:\temp2\out\fc10.shp') print ext


4

This question refers to the Getis-Ord GI* hot-spot analysis tool in ArcGIS. One can find an explanation of the tool here: http://webhelp.esri.com/ARCGISDESKTOP/9.3/index.cfm?TopicName=Hot_Spot_Analysis_(Getis-Ord_Gi*)_(Spatial_Statistics) The assumption that hot-spot is a synonym for heat-map is incorrect. Heat-map has a wide variety of definitions, whereas ...


4

The paper "Multiscale Analysis of Topographic Surface Roughness in the Midland Valley, Scotland" by Grohmann et al., 2011 describes the differences between a six methods of calculating surface roughness measurements from 2D digital topography. His paper was helpful since he provides a quantitative comparison of each method using a single test region at ...


4

For a problem this small the slopes are easily computed with a simple raster calculation. Given that the years are consecutive, let's name the rasters [y.1], [y.2], [y.3], [y.4], and [y.5] in temporal order. The slope grid is (2/10) * ([y.5] - [y.1]) + (1/10) * ([y.4] - [y.2]) For other than five rasters--but still assuming they represent consecutive ...


4

Surrounding topography does have an effect on each pixel as it is analyzed using the solar analysis tools. From ESRI's own documentation: ...accounts for atmospheric effects, site latitude and elevation, steepness (slope) and compass direction (aspect), daily and seasonal shifts of the sun angle, and effects of shadows cast by surrounding ...


4

from GRASS GIS: v.buffer: -c Don't make caps at the ends of polylines from the interface of v.buffer.distance in QGIS (Processing Toolbox): from the interface of v.buffer.column in QGIS (Processing Toolbox): or use GRASS GIS directly and not the GRASS plugin (as says zimmi)


4

You can use Data Driven Pages to quickly loop through each feature of your feature class. Within the settings you can set up what % you want to zoom to - i.e. 100% will have the feature fill the screen, but you might want to try something like 150%. This tool, although designed to make maps, is also useful for inspecting features quickly.


3

A key disadvantage of grid squares is that the sample rate is substantially lower along the diagonal vectors to those of the four sides (Jasons point above). If you have some regular linear pattern to your data the orientation of the grid affects the effective sample rate of each context. For example if you have a series of ridges and valleys, orienting ...


3

Yes, you can add further info, such as the coordinates of intersection points, editing the attribute table and calculating the coordinates with the field calculator using the geometric functions $x and $y.


2

Have you looked at Service Area and/or Closest Facility in the Network Analyst Toolbox? This may be helpful. The map you posted has desire lines, which you can find in the Business Analyst Toolbox. Another option, if you know which point goes to which facility, is Envelope to Polygon.


2

This is a volume question, rather than one of cartography. Here goes for a back of an envelope calculation: Of the two hundred and fifty five recognized countries or protectorates in the world, 33 have land below sea level. Most of these are only a few meters down (ref: geology.com). The approximate surface area of the sea is 360,000,000 km^2. So, ...


2

This is a bit old, but I was searching for solutions to this problem today (point --> line). The simplest solution I've come across for this related problem is: >>> from shapely.geometry import Point, LineString >>> line = LineString([(0, 0), (1, 1), (2, 2)]) >>> point = Point(0.3, 0.7) >>> point POINT (0.3000000000000000 ...


2

mean values are appropriate for that kind of analysis, however, you may also want to include max or min quantile/quintile statistics (probably easier to do outside of arc) if disease spread is dependent on min/max temperature etc. values. you will see which is your most significant variable when you look at the correlation matrix & run the regression ...


2

Assuming a route is a single polyline and you can have multiple points along it for any given time then one method is: Use spatial Join geo-processing tool with closest relationship and "one to many" set. Run summary stats tool grouping by route ID and summing the count field. Join the summary stats table back to the route layer by route ID.


2

As disclosure, I am SVP Marketing at Alteryx. Just FYI, we recently lowered prices for the desktop versions and also made them public: ...so you might want to look at most recent pricing before making a decision, and possibly talk to someone here. Shoot me an email if you want to speak to someone here further about the prices.


2

I have used this kind of socio-economic data for a number of projects. It can be very helpful to break out of the district polygons by laying a square grid over the area (side length based on either metres or minutes), and then using a script, calculate a score for each grid cell (e.g. if a grid cell straddles two districts, then calculate a cell value ...


1

Working with the IMG file directly in python is straightforward with the GDAL bindings. For example, you can read the data directly into a NumPy array: from osgeo import gdal geo = gdal.Open('imgn36w100_11.img') arr = geo.ReadAsArray() print repr(arr) array([[ 744.31896973, 743.68762207, 743.1116333 , ..., 550.42498779, 553.77813721, ...


1

The National Map Viewer used to use a different software platform, which made it possible to convert the data to GeoTIFF after selection of tiles. Some regional data provider do prefer GeoTIFF as well. But that may be of no help to you. However, the good news is that gdal is able to work with all three of these formats. ArcGrid is an ESRI binary format, ...


1

Given the Euclidean distance from urban areas, for instance you can reclassify it (0 for distance <= 50 ft, 1 elsewhere) using the Reclassify tool. (Alternatively, you can apply an equivalent conditional expression with the Raster Calculator... So you can choose the way you like). Then you should simply multiply the reclassified Euclidean distance with ...


1

The buffering tool creates a new output FeatureClass. What you could do is create a model that you run to create a buffer as a new temporary dataset in the in_memory workspace and then append that to your existing buffer FeatureClass. The assumption behind this approach is that you are creating buffers from NEW features and not features you have edited (e.g. ...


1

I'm not aware of a ready-made function that does this, but you can use the Processing Modeller in QGIS to build such a function yourself. To do this, you can run the "Vector/Overlay/Intersection" and "Vectors/Overlay/Difference" tool, both using the contours layer as the "Input Layer" and the glacier layer as the "Intersect Layer". The outputs of those ...


1

Intersect Layer 1 and Layer 2 (Vector > Geoprocessing Tools > Intersect) Dissolve result from step 1 (Vector > Geoprocessing Tools > Dissolve). Use name of properties/ id of properties as dissolve field. Add new field to result from step 2 and calculate area:



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