Lidar Lab

Module 5 (Supervised)

Was a little disappointed with this. The sandy areas in the coastline jumped into the urban classification. Perhaps I should have used a sandy area for a water example? The areas that weren't classified also jumped into the urban class (specifically the large green area in the NE central area of the original image) and the residential class too. Would capturing these areas and inserting them into the unclassified class have worked? I'm guessing that more classes would streamline these problems though.

Module 5 (Unsupervised)

Discuss the usefulness and pitfalls of image rectification.

Image rectification's main usefulness is being able to use multiple images from different sources to produce one comprehensive output. The main pitfall is human intervention! As we know, automation can produce pinpoint accuracies but the data is dependent on human input to identify and locate suitable control points. Like digitizing, the human factor diminishes the reliability of the data. Of course, the image may not help as it may be difficult to identify accurate points - we all assume there will be nice road intersections and giant 'X marks the spot' runways to set as control, but what if the image is in the wilderness or contains hundreds of water features? Also, unless the image has been corrected beforehand to account for distortions such as terrain, this may account for some further error in accuracy. The future looks bright though: increasingly higher spatial resolution data is being acquired that should ease the rectification process.

Why do the following features appear as they do in this thermal infrared image?

Image taken at 6.45am with a ground level temperature of 12C (54F). Now this is positively comfortable for a snow loving Brit like me! However, the time of day is important in this image as the night has cooled the area and the image largely shows gray tones relating to how well features have retained the daytime heat; the basis of thermal inertia. The storage sheds' roofs in the back yards no doubt get hot during the day but lose all heat at night thus appearing very dark. Same goes for the automobiles except where engines are currently running or just been used. Even with the engine running, the frame of the car will still appear dark. Vegetation also shows dark being cool but may vary in grayshade depending on it's moisture content and the content in the soil. So what's bright? Well, sidewalks and patios are brighter than most features as they take heat in quickly during the day but release it more slowly than others except roads in particular in this image. They are likely covered with asphalt which is an excellent absorber and retainer of heat thus appearing in the brightest shade of gray at night. Possibly only a waterbody could match that emission but there is no obvious one in this image. Finally, roof top bright spots are most likely hot vents from each house's heating system, unless some still have their chimneys active at 6.45am!

Module 2, Q7 Compare Displays

There are at 2 major differences between these displays that I notice.

1. The panchromatic has a clearer spatial resolution. The urban area in the multispectral is noticeably more blurry. The resolution is 10x10m to the multispectral's 20x20m.

2. The obvious difference of color as opposed to grayscale emphasizes the contrast between surface features, specifically the blue areas to the southeast that I'm guessing are grassland swamps. A river flows through it that appears to carry much sediment at the mouth which shows very well in the multispectral.

What problems might you infer or identify in using this type of photograph?

The major problem with this type of photograph are the colors. They are not what the human eye typically sees and therefore could have an impact on the decisions in identifying features. A major purpose of the map is to single out vegetation and it contrasts well with areas with no vegetation including water, however, with that, identfying other features could be difficult if the same tone appears in the map as in urban areas, with little contrast.

Module 11 - Google Earth

I chose the east coast of Lake Michigan for the high year round wind power class. This area appears to have very low human habitation so noise and shadow flicker will not be a factor. Bird migration traffic is low for this area and it would appear that environmentally, there would be little impact on landscape. This particular spot is where farmland is close to the lake edge - where building on existing solid ground will have little effect on natural areas along this coast and the wind power density decreases dramatically when moving inland here. Power produced by the windfarm could easily sustain the surrounding towns and farms year round. The golfcourse nearby may be the main source of complaints for this location!

Module 10 - Isohyet Map

I used the manual interpolation method but with the thickness of the contour line and for the sake of my sanity, I approximated the position of it between values. This method was good in the most part but still left a little guesswork but nothing that would alter the line dramatically, I hope! Also, manipulating the lines after drawing them was not much fun but necessary to iron out sharper curves.

Flow Map - Module 9

I chose the Quartic Authalic projection (which I selected in error thinking it was conformal - now I realise there would be better selections!) and deleted Antarctica as, well, they have no emigrants. I also played with the central meridian but Asia was split when I did that so not appropriate. I liked Joe's 'split continent' method but chose the conventional map for simplicity as only 6 values are mapped. Also, with so few values, I decided to create an 'actual value' legend. To me, the hand arrowhead is eye catching and makes me smirk every time I look at it, possibly because it reminds me of Monty Python! The thickest line value was 39pt and the smallest 0.7pt.

Contiguous Cartogram - Module 8

I went to town on this one - 13 iterations. Why? Because only a general recognition is expected of the world so this distortion would seem acceptable, besides, the one in the lecture was distorted moreso! I do not feel an inset map of the world is necessary. Identifying individual countries is not the purpose here, just the spatial pattern of the main contributors to the GDP and to show the differences between adjoining areas.

The most obvious anomaly here is Japan - the 'tongue' of Asia; this perfectly illustrates the impact that this one country has to the GDP and overshadows its neighbors, even accounting for the fact that adjoining countries post good figures too.

I played with various projections but settled for Robinson because of its minimal distortion of area and shape.

'Non-contiguous' Cartogram

Here's my proportional symbol map.....oops sorry, circular non-contiguous cartogram. I did try to create a non-overlapping feature shape cartogram but failed to discover in this instance how to do this or whether it was feasible.

Like the contiguous map, identifying individual countries should not be the intent here but the spatial pattern as described above.

Module 7: Dot Map

I determined the dot value of 2660 from the lowest county housing units value resulting in 2750 dots for the whole state! After some trial and error, I used a dot size of 0.8pt to fill 181 dots within the county with the highest calculated housing density; Pinellas. The basemap and dots were enlarged 160% to fill the width of the page. The dot was inappropriately small to be represented in a legend so the value was simply described. With the limitation of the page size and quality of the jpeg image, it is unlikely that original raw totals could be garnished from the map, however I did maintain a fairly high level of accuracy regarding the clusters where towns and cities are located by using an atlas.

Proportional Symbols - Module 6

Population Change by Division

The description for the map below also applies to this grayscale map except that D.C. does not need to be identified.

The classes are equal interval (and rounded) but I have also applied an equal interval to the shading where the difference between each class is 15% darker than the last.

I also softened each state border and created a bold black line identifying the division boundaries.

Population Change by State - Module 5

I changed the projection for all elements to Albers Equal Area to give a more realistic look to the states. I also highlighted D.C. to remove any ambiguity on the category it fell into. The natural break classes were only slightly amended to tidy the legend to 'cleaner' numbers. I did not deem a North arrow necessary for many reasons but mainly because I assumed the user knew where North was based on the cognitive view of the USA.

Mod4Lab - Map Lettering

Mod4Lab - Map Composition

A good map

This map has the key elements of north arrow, scale bar & text and simple legend. The map clearly displays its intent and has the spatial element of the major cities for reference without being intrusive. As a minor plus, it also shows the spatial adjacency to other states and labels those states (and Mexico) accordingly.

A Bad Map Indeed

I assume the red dots are towns? If so, how do I get to Mgarr? How far is it from Valetta and what road is it on? Maybe there are no roads? Oh dear, nevermind; I could always go to Circewwa by following the coastline... wait, it appears that town is now in the sea!! (NW corner of main Island, assuming a north of course).

Desirable Places To Work

I haven't yet been able to visit many places within the US since arriving in 2005 but I would say that a middle-of-the-road climate would suit me best after being brought up in a cold one and currently living in a hot one! The orange states too have caught my eye with some of their features.