A comparison of the education and health care opportunities for the people living along each route.

<p>(a) The total number of school aged children within 10km of each route partitioned by density and (b) the mean distance (km) to the closest clinic for people living along each route. The results show the Serengeti route connects the fewest children to schools and is the poorest at connecting rural people to medical facilities.</p

A comparison of the total distance of each proposed route, the current surface conditions, the amount of new paving, the number of major junctions, and the total human population the road would access.

<p>Note: The Serengeti Route requires the most amount of new pavement, would be the most costly to build, would contribute least to a national transportation network, and connects the fewest people.</p><p>A comparison of the total distance of each proposed route, the current surface conditions, the amount of new paving, the number of major junctions, and the total human population the road would access.</p

A comparison of the agricultural productivity of each route.

<p>(a) The spatial distribution of current crops (herbaceous crops are generally maize and beans for subsistence, shrub crops are generally cotton for cash), (b) the total amount of agriculture per kilometre of road; (c) the density of livestock and (d) the total livestock within 20km of each route. The potential for future agriculture depends on soil fertility and rainfall: (e) the distribution of soil fertility (estimated from the soil’s cation exchange capacity) and (f) the average soil fertility of each route; (g) the distribution of the mean annual rainfall and (h) the total annual rainfall along each route.</p

A comparison of the socio-economic demographics of the three possible routes.

<p>(a) The spatial distribution of economically active people and (b) the total number of economically active people within 10km of each route (Mbulu = 1,059,436; Eyasi = 919,297; Serengeti = 580,864); (c) the spatial distribution of unemployment and (d) the total number of unemployed people within 10km of each route (Mbulu = 904,930; Eyasi = 768,062; Serengeti = 458,037). The data suggest the Mbulu Route would connect the most unemployed people the largest centres of economic activity.</p

A comparison of travel times for the three routes based on the maximum allowable speed inside and outside protected areas indicates there is very little difference between the Serengeti Route and the Eyasi Route.

<p>A comparison of travel times for the three routes based on the maximum allowable speed inside and outside protected areas indicates there is very little difference between the Serengeti Route and the Eyasi Route.</p

The basis functions, <i>U</i>_{<i>j</i>, <i>k</i>}, for performing convolutions are constructed from Fourier modes on concentric circles.

<p>The parameter <i>j</i> determines the radial distance from the centre of the object, while <i>k</i> is the wavenumber. The images show the real and imaginary part of the basis function.</p

Example images.

<p>From top: Correctly detected wildebeest; Pattern and structure in the landscape frequently lead to false positives; The method is able to distinguish between different species; Species such as zebra, that have distinct body shapes are frequently not identified as wildebeest; The ability to distinguish between species is dependent on sufficient training examples, here the algorithm has misidentified a flock of juvenile ostrich as wildebeest.</p

Comparing the performance of automated and manual counters.

<p>(A) Root mean square error of counts. The correct count for each image is assumed to be the third and final count. Average per image error is shown for the algorithm (blue line) as a function of the number of training samples from the 2012 survey that were used. For comparison, per image error is shown for each of the first pass human counts (red, green lines). (B) Total wildebeest counted within the image set. The final count is shown by the dashed line. The algorithm (blue line) outperforms both human counters in attaining a closer estimate to the true value. This is because the algorithm exhibits no systematic tendency to over or under count. It should be noted that 3000 was the maximum number of training samples available, and it is plausible that the automated total count will drop below the true count before it asymptotes. (C) Individual image errors. The black line is the <i>y</i> = <i>x</i> line for reference. While average per image errors are comparable between automated and human counters, the algorithm makes large errors in a small subset of images. Images that contain many false negatives tend to be darker than the training samples, while false positives occur when there is a lot of structure in the landscape. (D) A comparison of image light levels and under counting. A linear regression shows a significant negative relationship between image light level (average of value component of HSV image) and the amount of under counting (<i>β</i>₁ = −1.37, <i>R</i>² = 0.12). The under count fraction is calculated as and images for which algorithm count > true count are excluded. Point sizes are proportional to the absolute value of the under count of wildebeest in the image.</p

Confusion matrix.

<p>As the accuracy based on the total count does not indicate precision or recall, performance metrics were recorded for a random subset of 100 images. Negative totals are based on the number of non-overlapping regions within each image that are approximately equal in area to a single wildebeest. From these results: precision , recall .</p

Comparison of counts between manual and automated methods.

<p>Comparison of counts between manual and automated methods.</p