Week 10: Protecting Critical Infrastructure

This follows on from last week. This time we had to use the elevation data and create lines of sight from the NORAD tunnel entrance at Cheyenne Mountain, Colorado for people protecting the entrance. It was quite interesting to see this work, although my 4th deliverable, supposed to have been created in ArcScene (the 3D view), only would work in 2D as a jpeg and I'm not even sure I got it to work correctly.

However, in 2D, here are a couple of images: the grey one with the red-and-green lines shows an aerial view of the NORAD tunnel entrance with lines of sight (green = visible, red = impeded). The other one is a conventional map view with a 3-mile buffer round the NORAD site and a 500-foot buffer round the nearest airport, or rather, heliport.

Week 9: Data preparation for Homeland Security

This week we had to prepare a series of data layers for next week's Homeland Security lab. In fact much of the work was done for us, but there were still some challenges. I didn't have trouble unzipping data or finding enough space on my H drive (although I may soon run out of space), but the challenging part was more about working backward from the end of the instructions (within a given section) when I couldn't make complete sense of what the end of the section should be. The road section directions were a problem for me so I ended up doing much of the original directions to get to the same endpoint. The lab was useful for getting an better understanding of how to group layers, and I am delighted that I was able to rectify the projection of one raster that initially was a long way away from the site in Colorado. And finally, I am glad that we are learning about how to organize the data and the environments within ArcGIS.
One map is just the screenshot which is intended to display the group layers that we created (or manipulated). The other, which is a "real" map, i.e. containing layout items, is a larger-scale map showing the location of the NORAD site in relation to the elevation. I didn't want to change the labels of the group layers in case I didn't change them all back correctly, so they are labeled with my initials as per the layers.

Week 8: Crime in Washington D.C.

This week we did proximity analysis to examine, first, various categories of crime in August 2009 in Washington D.C. and the relationship between the crimes and the location of police stations, and second (shown here) where juvenile crimes were committed in 2009 in D.C. and how that related to the 1000-foot-radius "drug-free" zones around the schools that Washington has mandated. In fact I am uncertain about whether the drug-free zones are effective as drug-free zones, since the vast majority of the offenses listed are non-drug-related. Moreover, we don't have data that describes the levels and distribution of crime before the drug-free zones were established. Given that 1304 offenses are listed as having been committed within the 1000-foot buffers round the schools, they don't seem to be fully effective as "crime-free" zones. That is an average of 10 offenses per school across the year, or a bit less than one per month - not a high number, but not zero either.
In the map I thought it would be interesting to see whether there are higher rates of crimes around high schools than elementary or middle schools, which is why I symbolized the schools differently. However there does not seem to be much relationship: The highest numbers of offenses are near two high schools and one elementary school. Several other high schools have low rates of crime, and the rest of the elementary and middle schools have either low or moderate levels of crime.

Week 6: Location Decisions

For much of this lab I felt very frustrated - suffering from jet lag and working with a Really Slow laptop. Also I didn't discover a vital starting point for Project 2 until I had completed almost all of Project 1 (in an effort to discover the starting point for Project 2) - and I have run into more than the usual roadblocks. However I did get to the end in time. So...

This lab was about a hypothetical couple making a decision to relocate to Alachua County, Florida to be closer to family, with other factors also included. The lab involved calculating distances, reclassifying the distance calculations, and producing weighted overlays that would come up with the "best" census tracts in the county for this particular couple.

The base map (not shown ) is of various features in Alachua County that the hypothetical grandparents (with grandkids who live in the selected-in-red census tract) might want to be close to. This was a fairly basic map.

The second map (see top right) was probably the most complicated one I have done so far. I still need to learn how to work with map templates, as the arrangements were complicated for me and I removed all the four data layer displays in order to make the succeeding map. This second map (one map with five layouts, or five layouts with one map, don't know which) shows the distance from various features that the grandparents might be interested in - North Florida Regional Medical Center, the census tract containing their grandkids, bus routes, community centers, and the University of Florida.

I eventually got the models to run correctly - or at least to run and to display and to acknowledge that they existed, which initially they refused to do - I briefly posted a screenshot on this blog that said ArcMap said the model had run successfully, as proof in the absence of any other! I am troubled by the models though because when I reversed the scales, the bottom number (the 9) wouldn't reverse. But I couldn't get the model to run properly when I tried fixing those numbers in the model prior to running it, so eventually I left the numbers alone and just reversed them with one click. The map at top left shows the final weighted overlays from the models.

I am still a bit confused about whether I have done the reclassification correctly. Not all the distance calculations yielded the same distance per category, so I ended up with some smaller numbers of categories. I'd like to understand this better but after spending about 20 hours on this+project 1 (I blame this partly on the laptop; I didn't have a chance to print out all of the pdfs before I left town and was unsuccessful in trying to connect remotely on other people's computers so that I could print the pdfs elsewhere, so for this lab I have been reading the project1&2 pdfs on screen, which is dismayingly slow to advance each part of a page. Hence I didn't see the note about which census tract to use for project 2 until I had worked my way almost all the way through project 1 looking for the answer), I didn't feel I had the time to ask more questions about it.

On the plus side, I learned a lot in this lab and I feel good about managing to get a lot of information in fairly clear format on to one page. I am grateful for the information about setting environments and organizing data within ArcMap. (Now, about those map templates...)

Week 5: Impact assessments

This week was more relaxed, fortunately. The readings were about urban planning and impact assessment. The two maps shown are from an ESRI lab. The first shows location quotients for agriculture/forestry/fishing in local government authorities within the planning area of mythical Pewter City; the other shows the occupancy rate of university students, also in Pewter City. Location quotients are familiar to me because I did a project comparing the demographics of areas around greenway trails to those in towns where the greenways were found - essentially calculating greenway location quotients. So you would think this was easy, though I initially found it confusing because of all the acronyms, especially labels like AG_FRS_Ref for "agriculture, forestry and fishing in the reference area". Eventually my brain kicked in and I was able to complete this one. ESRI labs are generally very straightforward and this one was too, although they don't make you think nearly as hard as the regular labs. But that's a good thing for me this week.

Week 4 Oil Spill - GIS response summary, and Animation

The animation was part of our participation exercise and produced a six-frame animation of the extent of the oil spill, from 29th April 2010 to 26th May 2010.
The link to the animation is here: http://students.uwf.edu/db27/OilSpillExtents.avi

The hard part for me was getting the projections to line up. I finally did that by clearing the projection information for each oil extent shapefile individually, redefining the geographic coordinate system to what it said it was before, and then projecting it to the same projected coordinate system that it said it had before. Don't quite know why this worked, but I was thrilled to see that it did. The background files were unprojected, and when I projected them to the same coordinate system (that the oil spill files said they were originally, NAD UTM 16N), it all worked together. But not until I had tried every other unsuccessful combination of projection-on-the-fly, adding the (first projected) background files and then adding the (apparently projected) oil spill files, etc. etc. Once it worked though it was great.
An improvement on this animation would be to have the date of each oil spill extent display. I tried labeling in ArcMap, for the states and a label of the range of dates, but in animation all these labels continually flashed on and off, so I deleted the labels. There must be a better way!

On the role of GIS in disaster response, specifically in regard to the Deepwater Horizon oil spill in the Gulf of Mexico:
GIS has an essential role in disaster response, and its role is immediately visible to us in the oil spill maps in the New York Times and in this week's lab assignment. Below is a list of GIS involvement in disaster response, in general, and in the case of BP oil spill. After a disaster we can expect GIS to play an integral role in:
1) Showing the location of the event and the spread of its impace. In this case, GIS has been used to map the oil spill, both as oil on the water and as oil plumes.
2) Showing the location of vulnerable populations and their habitat. This could be people in homes at risk from a flood or an earthquake (and should have been prepared before an actual disaster). In the BP oil spill, the vulnerable populations include marine life, birds, shore animals. It also will include information about fishing grounds and other places (see below) at risk since the livelihood of fishermen and others whose livelihood depends on the ecology of the Gulf is also threatened.
3) Showing locations where there is damage or harm, to people and/or habitat. After an earthquake this could be where buildings are damaged; since the oil spill GIS has probably been used to map where oil-soaked birds or marine mammals have been rescued or found. Such mapping also gives more specific and recent information about vulnerable population whereabouts.
4) Showing the location of measures taken. In the case of the oil spill, examples might include where oil booms are, or are recommended (and whether they are one or the other) and information about cleanup (see below)
4) Showing where assistance centers are, from hospitals to FEMA offices to animal shelters. In the case of the oil spill, it might include FEMA offices, wildlife rescue sites, places for volunteers to go, other government cleanup staging areas, etc.
5) Educating the media in all sorts of ways: about where the damage is, the extent of the cleanup, etc. etc. An oil spill example is the GIS map of the extent of the closed-to-fishing boundary, an updated version of which has been shown daily in the New York Times.
6) Helping to direct the cleanup, in terms of recording where what is needed and what it will take in materials and people and costs, as well as recording locations where workers have gone to clean up and the status of those efforts. In the case of the oil spill, this would include a lot of coastal information about the booms, the wildlife cleaning and marsh protection efforts, and more.

Week 4: Oil Spill in Gulf of Mexico

This week we had two tasks. The first was to display in Google Earth the area closed to fishing on two dates (25th May, 1st June) after BP's oil spill in the Gulf of Mexico, and to calculate the difference in square miles between the two areas. The second, some maps from which are shown here, was to create maps of several environmental sensitivity and environmental impact/effort indices for a chosen section of the coastal panhandle of Florida. I chose an area that encompassed parts of Gulf and Franklin counties.

The first map (or rather the one at left) shows types of shoreline, from sand to wetlands, coded according to their sensitivity to an oil spill. A second map (top left)shows some of the creatures at risk from an oil spill, and the areas where they are likely to be found. Another map (top right) shows federal and state jurisdictions, as well as boom operations (already staged and only proposed: only one boom of 23 was coded as staged). Shoreline on only one of the two counties is under federal or state jurisdiction, which might create conflicts at a time of crisis management like this.

My biggest technical problems were in downloading an elevation file (not shown, though it did eventually appear) and in creating a graph of the different shoreline types and lengths. I did produce the graph, but I couldn't get it to display colors that matched those in the map, apparently because ArcMap will only do this if the table from which the map is produced is spatially defined. I couldn't figure my way out of this one.

Parts of the lab were frustrating but it was tremendously interesting to be dealing with such a current and urgent topic. It was also enlightening to see how much preparation has been done to be aware of the aspects of the environment that are at risk and to be ready to prevent or at least mitigate that risk.

O

GIS applications Week 3: Hurricanes

This week we were to look at Key West, Florida's flooding from Hurricane Wilma's storm surge. I was initially panicked, owing to the fact that I did Project 2 first because it wasn't clear to me whether to do both 1 and 2 but I certainly needed to do 2, and only afterwards did I find the Project 1 work doc (I had thought I had printed out all the docs from both projects and hence had all the instructions I had to go on, but apparently not) which explained pretty much everything I should have done. In any case, I did come up with all the deliverables for project 2, although the table and graph are from Excel since it was quicker and the Project 2 directions didn't specify requirements for creating the table/graph). When I got to the second quiz, the one focusing on Hurricane Katrina, I discovered the Project 1 work doc and worked through that to get to the answers I needed. It made the route a lot clearer. Ah, hindsight...

We had to display a map of elevation and bathymetry of Key West, which showed how low the island is. After this we had to determine what proportion of the island was flooded by an 8-foot storm surge, and make a graph of the flooded land by landcover type. Key West turns out to be heavily developed (densely networked with streets and classified as developed land) across nearly the whole island (the landcover map is the 2nd map on this blog). All I know about the Florida Keys comes from Carl Hiaasen, so this was a surprise to me - but correspondingly, most of the land that was affected was developed land. Only two areas were free of water and both were quite small.

Moreover, all but one of Key West's churches and schools, both of its hospitals and its only airfield are in the low-lying (under 8 feet) area (see the first map). This was a surprise to me too. I don't know how many hurricanes have hit the Keys but there must have been a few and there are certain to be more in the future.

http://students.uwf.edu/db27/Bobwhite.ppt

Final Week: the Bobwhite/Manatee Florida Transmission Line Project

As a start: here is a link to the final Powerpoint about the project: http://students.uwf.edu/db27/Bobwhite.ppt

and just in case it's not retrievable otherwise, here is the link to the pdf:

http://students.uwf.edu/db27/Bobwhite.pdf

Here is a map from the Powerpoint ( I had trouble getting this link to show the revised ppt which includes this particular map, so I put it in here just in case):

Hmm. What to say about this that I haven't already said in the essay and the Powerpoint? For this project we were given some background information about Florida Power and Light's efforts to get approval/certification for an electrical transmission line that would run through part of Manatee and Sarasota counties in Florida. We were also given some data about the project (the actual, somewhat revised corridor for the project was approved in late 2008) and asked to find some more.

We were required to do four things: 1) evaluate the extent of the conservation lands and wetlands within FPL's preferred corridor; 2) count the number of housing units in the corridor and a 400-foot buffer around it, and also count the number of land parcels within the corridor and the buffer; 3) count the number of schools and of daycares within the corridor and buffer; and 4) measure the length of the corridor.

This was an enjoyable project because of the variety of work it entailed. Doing the first part (conservation lands, wetlands) was pretty straightforward - see the map above. Counting housing was more problematic - we did it from aerial photographs - because there were some buildings that looked from the air like what in North Carolina we would call tobacco barns. I didn't count every structure; I tried to count the ones that looked as though there was regular access (driveway, space cleared around the building) as housing. I noted a caveat in the essay because of this and the fact that there was no date for the aerial photos. Parcel counts were easy given the selection/calculation functions in ArcMap, though it's hard to say what is within and what is without the corridor when many more parcels intersected than were entirely within the corridor or buffer.

Schools and daycares were easy to exclude given the useful data from the Florida Geographic Library. Measuring the transmission line was a bit more of a challenge. I found it difficult in ArcMap because the measuring line would disappear when I panned the screen to get to the next section. Instead I found the same area in Google Earth, whose measurement tool is more user-friendly for multiple-screen uses (at least for me), and measured it while looking at the ArcMap aerial photos as well to compare the ground view. I later measured it again in ArcMap, adding short distances together, and got the same result but with more effort.

Thanks for the opportunity to do these labs during the semester. This in particular was a really interesting absorbing project.

Week 11: Labels, Spatial Analyst and 3D Analyst

We had several tasks this week and five maps to produce. The first section I worked on was Spatial Analyst. We had to create a model to conduct spatial analysis of an elevation raster - creating slope (steepness) and hillshade (shows shadows based on elevation and orientation relative to the sun) and aspect (shows compass direction of each raster cell). ArcMap turns out to have three different ways of building such models: using the tool in ArcToolbox, which involves filling out the usual dialog box for input and output; dragging-and-dropping using the ModelBuilder (see the graphic with rectangles and ovals above), and creating the code using the command line. I would describe ModelBuilder as clunky and the command line approach as elegant, in part because the code lets you see what you're doing and ModelBuilder just says "Please do something with this input". But they all seem to work. This short lab section was relatively straightforward.

Second, we had to convert vector line and polygon files to raster files and reclassify those raster files. The largely purple graphic above shows the converted and reclassified vegetation map of a vegetation study area in/near Harlan, Kentucky. I was a bit confused because the white spaces within the study area were originally, in the vector file, "altered/developed" land; we had to reclassify this as NoData, which meant that in the reclassified raster version all that land comes out as white as well as the small portion that was NoData before (hence my note at the bottom of the map).

Third, we had to produce two maps of the Tampa, Florida area, using a variety of labels and annotation to create the map. I have used labels a little in ArcMap but never felt I knew what I was doing, so this was directly useful for me. The Cities and Roads map used different symbols for different kinds of roads; the Paddling Trails & Bird Sanctuaries map also used annotation. These maps reminded me of how tricky it can be to get all the information you need on to a map without crowding it - the Paddling Trails map of course needs roads as well as they are such an integral part of the landscape (and how else do you get to the rivers you want to paddle?) although without the roads it is prettier! I had a lot of difficulty passing the ESRI exam on the labels&annotation section; it's confusing and the only easy part was decoding the Vbscript code. I'm looking forward to the programming class.

The final map, at the top left I think, was from the 3D analyst section. It shows a hiker's-eye view of the cabin ( the red dot) and the two "observers" (the other two dots; this made me think of military/police exercises!). The black snaky lines are actually contour lines - I couldn't resist using the "Contours" icon when I found it, although I had no idea how to use it or how to remove the lines. The white contour line at the top is the last one I created, and since I didn't know how to remove it and it was the last one, it's still white. The 3D function in ArcScene was very interesting - when I wasn't frantically trying to stop the navigation tool from turning my mountains round and round - although I don't have an idea of what I could do with it in real life at this point and I do wonder how useful 3D is given what one can interpolate from 2D maps. The other thing that intrigued me greatly was seeing the overlapping transparent 3D rectangles that make up the 3D display before they had finished drawing the map completely.

Week 9: Vector Analysis, Part II

This week's lab was all about creating and manipulating buffers around lines (roads and rivers) and polygons (lakes). Given a set of shapefiles for roads, rivers & lakes and for conservation areas, we had first to create buffers of varying distances around each of the three features (roads, rivers, lakes). Second, we had to combine these buffers through a "union" function, and finally, we were to create a new shapefile that included all the areas that were a) within the different specified distances of each of those three features but b) not within six conservation areas. a) was represented by performing an "intersect" function on the combined buffers, and b) was achieved by performing an "erase" function on the result of a). Along the way there were a few other manipulations of buffers.

This was a pretty straightforward lab for the most part and it was interesting to see that we could create buffers of different distances for different attributes within the same feature. My greatest difficulty was working with ArcGIS to try to calculate the areas of the final polygons because I couldn't initially get the Area field to be wide enough to include all the numerals. Eventually I tricked it (or so it seems to me), by giving the field a longer name, into giving me the answer I needed.

Answers to the lab questions:

1) Using "Intersect" gave me the same result on the map as using the records within the union of Roads and Water that were within both the roads buffer and the water buffer. The table for this layer contained four records, like the buffers_union_export file (before that file was converted to a singlepart layer, which had 82 records).

2) To exclude conservation areas from the project, I used "Erase", because it cuts out the conservation areas that are within (that intersect with) the buffer area.

3) The possible_sites layer, the final result, has four records, but two of them are the same on the map (in the table they are identical except that one has a buffer distance of 150 and the other of 500, and they have different FIDs); this part makes me wonder whether I have done something wrong as I don't understand exactly why I have (almost) duplicate records. The area of the largest feature is 60,783,617 square meters; the area of the smallest (duplicate) feature is 585,748.

Week 8.5 (Spring Break): Gulf County Land Ownership

For the spring break week we could do this as an optional lab. It was largely about using raw data and joining tables: we had to join a land parcel file that had no associated data to a tax roll table that included ownership data. After that we were to calculate the acreage for each parcel and sort the data table to find the four largest landowners, which are shown on the accompanying map.
This was a fascinating lab for me because 1) Gulf County is the county I was assigned in week 5, 2) as is clear from the map, a very small number of landowners own nearly all of the county, and 3) it's clear from the map, in which I left the boundaries of each parcel clearly marked, that the great majority of the county was divided up in a very systematic way - it looks as though the division is in approximately 640-acre sections. I don't know whether the division was for the purposes of development (my first thought) or farming (my second, after I saw the land parcel size) but it was very striking. It reminded me immediately of Carl Hiaasen's terrific novels about rapacious developers and other lowlifes in Florida - not that I know anything about the specifics of this county. It also reminded me of how helpful a map can be in making sense of a complicated table, or at least in highlighting some of the table's more interesting features.
The lab itself was quite straightforward. I think the map is a bit more crowded than I hoped, but I wanted the county itself to be as large as possible while still fitting in the legend. I included public land (the crosshatched part) to show how that was a large chunk of the remaining large-landowner parcels. The St. Joseph acreage also includes the entries listed as "The St Joe Company", since that had the same two contact people and the same address as "St. Joseph Land Development Company". I didn't combine the entries for TIITF with those for TIITF/GFWFC because it wasn't clear to me that they were the same thing.

Week 7: practice with digitizing features

This week we were to take last week's data files (two raster aerial photos of the UWF campus and a buildings and road file) and modify them by a) creating a new file (athletic fields) and digitizing its contents from the aerial map, b) adding one building via digitizing to the buildings file, and modifying the shape of two others, and c) adding in a road from the aerial photo - the one in the southeast corner that connects the larger road's oxbow ends - again, via digitizing.
This was a useful second part of the lab exercise after the first part, which was to complete the editing module in ESRI's Virtual Campus class following ESRI's step-by-step directions. I had a short period of panic when I added my files one by one only to discover that one of the aerial photos was invisible (because I'd forgotten to do the last step in georeferencing in the previous week's lab, I think) but eventually I got that sorted out. I am also glad to learn a bit more about the actual properties of the files we create and edit.

Week 6: Georeferencing raster datasets

For this week's lab we were given two raster datasets - they are adjacent aerial photos of the UWF (Pensacola, I assume?) campus - which had no accompanying georeferencing information, and we were asked to line them up with two already-georeferenced buildings and roads GIS layers. The accompanying map shows the two photos, which line up almost perfectly with each other (I drew a blue line across the map at the point where the two photos meet to make the division a bit more visible) and quite well with the buildings and roads layers.

Georeferencing the first raster (the one at the top) was straightforward: we just had to find individual points on the aerial photo and match them to points on the buildings or roads layers. The second raster was a bit more difficult because it had been intentionally distorted from the original shape, so after finding a few matching points we had to switch from using a 1st-order polynomial function (one click in ArcMap) to a 2nd-order polynomial, which allows the raster to bend and warp a bit in order to accommodate the georeferenced layers. I briefly tried a 3rd-order polynomial but it made things on the ground seem to fit worse, so I reverted to 2nd-order. The error stayed fairly low, but the distorted raster was resistant to the idea of lining up really perfectly. However I think it looks quite faithful to the buildings/roads layers in the .jpeg.

Apart from the relief of a lab that was less effort than Week 5's fortnight-long grind, I was thrilled to try georeferencing in ArcGIS - I tried it once last year at home with an old topo map that I had scanned in my home printer/scanner, but those results were a lot less satisfying. Evidently having the right source material, not to say the right tools, makes a difference.

Week 5: Searching for data on the web for Gulf County, Florida

For this week's fortnight-long lab we were each assigned a Florida County - mine was Gulf County, which is on the northwest Florida coast - and were required to find data on the Web for that county. We had to find 11 separate kindsn of data: county boundary; cities and towns; public land; roads; hydrography (one line file, one polygon file); two of four landcover/plant/wetland data files (I chose strategic habitat conservation areas and land cover); and adjacent raster datasets that included any four (making up a full quad) digital ortho quarter-quads (DOQQs), any one digital elevation model (DEM) adjacent to the DOQQ, and any one digital raster graphic (DRG) adjacent to the DEM. We were allowed to create a maximum of three maps and three maps is what I created.

Providentially most of the files were available through the FLorida Geographic Data Library, in usable format with the Albers Equal Area Conic projection (which I kept). The first difficulty was in trying to find and then download the raster datasets (DEM, DRG, DOQQ). I still haven't mastered FTP file transfer, and had trouble unzipping some files. I also had some that I was unable to reproject properly, although it's not visible here. Unfortunately although each of my raster datasets is adjacent to another one of a different type, they're not adjacent in the order described above.

The other difficult thing was organizing my files in ArcMap and settling on a layout. I need to learn to manage my files better. I do keep a paper trail of what I've downloaded, but I need to learn how to manage new data layers (Do I need to open a new ArcMap document every time I want a different map? Can I make extra data frames invisible in the layout?) and I am now wary of the Bookmark function, since after I created bookmarks, in Data View, I pressed Bookmark while in layout view and it irremediably shrank my map. Back to square 1...

But on the plus side, I have learned quite a bit about finding GIS data on the web.

One note about the maps: I used the maximum permitted three maps because there was so much data in the hydrography maps (though I removed some values) and land cover (I generalized some categories). Some of the data in these files overlaps - wetlands, marshes, streams, conservation areas - so it's confusing to have it all on one or even two maps. Maybe if I were more proficient I could do it though!

Haiti map

This map is from a site called Relief Web http://www.reliefweb.int. It was not my first choice but the best maps I found were in pdf. The map here, which may be hard to see adequately in this jpeg, was compiled on the 12th of January 2010, and shows potentially affected population areas (population increases from pale blue to dark blue and then yellow to red to purple; the legend is hard to read here) in the earthquake-hit regions. Also indicated is the magnitude of earthquake shocks in concentric circles - blue to yellow to red as the impact increases - various population symbols, and some other items such as the location of World Food Program offices.
This is a fairly small-scale map so it doesn't show great detail and wouldn't/shouldn't be used for, say, finding a route to the World Food Program office, but it does indicate how powerful the earthquake has been and how large the population is that has been affected.

Week 4: the mysteries of map projection

This is my Week 4 result. The intent was to produce three different map projections of Florida, as well as a table (I created it in Excel and pasted it into the ArcMap layout) that showed figures for the area of each of 4 Florida counties. You may not be able to see it in the graphic, but county area differs from 1 to nearly 20 square miles depending on the map projection used (Miami-Dade, down at the bottom of the state, had the greatest discrepancy). The discrepancies are significant, but they are not big enough to be visible on these small-scale (and small!) maps.
Most of this was relatively straightforward. I removed the visible frame around each data frame so that it wouldn't look so strange having one north arrow and one scale bar for three maps. Not shown is the aggravation I had trying to reproject the undefined aerial photo (not shown in this graphic) of the UWF campus. Although I managed to get something to display in the correct county, I was unable to redefine the original file correctly. Projection still seems a bit like magic to me, magic that I haven't yet mastered.

Creating the first map, Mexico: population by state, taught me how to create a new shapefile from a selected part of another shapefile, a technique I look forward to using in the future.
I thought I was doing well with the map until I looked at it on the blog and realized how cluttered the labeling was and how poorly the word Mexico shows up on the country. However doing the second map in this lab, the one of central Mexico, taught me a bit more about that. I did have a lot of frustration trying to get the labeling right as initially I couldn't get the map to display only urban areas with populations over 1 million - despite checking all the right boxes, I think - and only when I switched to data from layout view was I able to get it correctly labeled. The great thing about this map was that I learned to do an inset map properly.
For the third map, the stretched symbology elevation map, we looked at a raster file of elevation for Mexico. I had to check an atlas before concluding that the figures must be in meters - I didn't see it anywhere in the metadata. I would think that stretched symbology, which is shown here, would be a better way to depict elevation than classified symbology because elevation changes continuously rather than jumping from elevation category to elevation category, but I actually found the classified symbology map - not shown here - easier to comprehend. I did wonder why the raster file had some small omissions, or empty spots, where Mexico had no elevation information; this is probably only barely visible on this map.

Week 3: GIS cartography via maps of Mexico

Creating this first map taught me how to create a new shapefile from a selected part of another shapefile, a technique I look forward to using in the future.

I thought I was doing well with the map until I looked at it on the blog and realized how cluttered the labeling was and how poorly the word Mexico shows up on the country. However doing the second map in this lab, below, taught me a bit more about that. I did have a lot of frustration trying to get the labelling right as initially I couldn't get the map to display only urban areas with populations over 1 million - despite checking all the right boxes, I think - and only when I switched to data from layout view was I able to get it correctly labeled.

For the third map we looked at a raster file of elevation for Mexico - I had to check an atlas before concluding that the figures must be in meters - I couldn't find this anywhere in the metadata. I would think that stretched symbology (which is shown here) would be a better way to depict elevation because elevation changes continuously rather than jumping from category to category, but I actually found the classified symbology map - not shown here - easier to comprehend. I did wonder why the raster file had some small missions, or empty spots, where Mexico had no elevation information; this is probably not visible on this map.