Lab 15 – Dasymetric Mapping

Dasymetric mapping is a mapping technique that involves using ancillary spatial data to improve the visualization and accuracy of spatial phenomenon that is known to not be uniformly distributed throughout the landscape.  An example of this is population density, which is presumed to vary greatly spatially within the census tracts which are used to estimate density.  By using smaller aerial units, say census blocks, we know that the population density is not uniform within the census tract.  By using land cover data, the accuracy can be improved further, as we presume that population density is zero in forest areas and water bodies otherwise not accounted for.

In this lab we were given census tract data and high school boundary data and told to determine the approximate number of students who fall in each school zone.  The issue is that the census tract polygons and the school boundaries do not align in any useful way.  By splitting census tracts that fell within different high school boundaries, then weighting the population estimate based on the area that fell in each boundary (i.e. aerial weighting), a relatively accurate and easy-to-produce estimate was determined; error was at 10.95%.  Then, by incorporating land cover data, specifically imperviousness, the error decreased to 10.19%.  The imperviousness of each split census tract was used to weight the proportion of the entire census tract that would be assigned to it.  Using the dasymtric mapping method was useful for projects like this, because surveying a community individually would require large amounts of time and resources, though accuracy would be higher.

Lab 14 – Spatial Data Aggregation

Gerrymandering is the manipulation of political boundaries so as to favor one party or class.  The favorable outcome is a result of the statistical influence of scale and zonation of areal units.  This manipulation of boundaries often produces odd shaped districts.  The two images to the right show two outcomes of gerrymandering.  The top image depicts a district boundary that chops up a county.  The bottom image depicts a very elongated, irregular district.

Lab 13 – Effects of Scale

Quantitatively, I compared the DEM elevation values directly, as well as two derivatives: Aspect and slope.  I first made both an Aspect and Slope surface for these DEMs.  I then used Batch processing for the Get Raster Properties tool selecting MINIMUM, MAXIMUM, and MEAN for each of the now six total rasters.

The elevation data comparison shows that the LiDAR DEM had a greater range of values compared to the SRTM DEM; the minimum value was smaller and maximum value was larger for the LiDAR DEM.  This may be an indication of greater elevation resolution.  The mean elevation value was lower for the LiDAR DEM.  Though these differences are apparent here, the magnitude of the difference is small and not likely significant. 

The slope and aspect summary statistics are highly similar between the two datasets as well.  The mean slope of the SRTM DEM is smaller, though this can be inferred based on the smaller range in elevations of this DEM.  It may be assumed that the LiDAR data is more accurate; however, the overall difference between the two datasets is very minimal.  I suspect the LiDAR dataset is more accurate for two reasons: First, it is the product of a resampling technique whereby the underlying accuracy of the high resolution 1-m DEM is certainly higher than the derive 90-m DEM.  Second, the SRTM DEM was created via orbital spacecraft, which, inherently introduces a higher degree of vertical measurement error.

Lab 12 – Geographically Weighted Regression

Spatial regression can be used in GIS to model a phenomenon of interest.  In non-spatial regression analysis, spatial auto-correlation is generally undesirable.  Spatial regression attempts to quantify auto-correlation and use it as an explanatory variable.  Geographic Weighted Regression (GWR) is a specific spatial regression used to account for multicolinearity.  In the lab this week we compared GWR to OLS regression.  The model output from GWR regression was better (i.e. had a lower AICc) than the OLS model.  Further the z-score was lower, indicating that there was spatial dependence in the phenomenon of interest.

Lab 11 – Multivariate Regression, Diagnostics and Regression in ArcGIS

Regression analysis can be used in ArcGIS to model a phenomenon which may vary spatially.  There are three main reasons for regression analysis: 1) to predict values in unknown or un-sampled areas, 2) to measure the influence of variables to a particular phenomenon, or 3) to test hypotheses about the influence of variables on a phenomenon.  It is very important to test the usability, performance, or predictive power of a model due to its potential in policy decisions.  There are several diagnostics to test the performance of a regression model that go past simply determining the R-squared value (which may be very misleading).  ArcMap contains an Ordinary Least Square regression tool, among others, which produces some of these diagnostics, however it is up to the user to evaluate these statistics in the context of the model.  The six step process outlined by ESRI to interpret these statistics provides a foundation for determining the "best" model.  One important thing to note is the frequency distribution of residuals in a histogram.  The residuals should be distributed standard normal.  A positive or negative skew may be the result of spatial auto-correlation.  This is perhaps the biggest use of regression analysis is GIS, as spatial analysis is central in ArcMap processing.

Module 10 Lab: Supervised Classification

Above is a supervised classification of Germantown, Maryland generated using
ERDAS Imagine supervised classification.  The map was ultimate composed in
ArcMap.

1.      I used the seed polygon method to generate the signature polygons.  A distance threshold value of approximately 25-50 was generally used.  In cases when only one class was created and the class was relatively obvious, for example water, then I used a higher distance threshold value.  In cases when there were many classes for the same classification, for example Agriculture 1-4, I used a lower threshold values.  The lower values and many classes allowed for high coverage with minimal (or none) mis-classification.

Module 9 Lab: Unsupervised Classification

A map depicting the unsupervised classification of University
of West Florida campus.  There are four different Information
Classes.

Lab 9 – Accuracy of DEMs

Like any interpolated raster surface, a DEM is derived from an array of measured points, specifically elevations.  What is important for interpolated surfaces is the error introduced during the interpolation process, thus the accuracy and predictive power of the DEM.  The accuracy of a DEM can be determined by comparing interpolated values to known/measured co-located values.  These points can be surveyed after the DEM is created or can be withheld from the interpolation process and used to validate the model.  One method of collected data for DEM generation is via LiDAR.  To check the accuracy of LiDAR-generated DEMs it is important to test several land cover types individually, as they may have different relative inaccuracies.

Module 8 Lab: Thermal & Multispectral Analysis

Multispectral image of Coastal Ecuador highlighting the
color contrast of several distinct features.

The portion of the map that I selected is an agricultural area of the river bank.  I chose this entire area because it was a diverse area, thus providing a good contrast of land features.  There is a freshwater river with a forested island, and on the main land is an agricultural area surrounded by an urban area.  The band combination that was selected appropriately highlights these different features.  Red was linked to Layer 4, Green was linked to Layer 2, and Blue was linked to layer 6 (Thermal IR).  Accordingly, the vegetated island (high NIR, low temp) appear bright red, the urban areas to the east (low NIR, high temp) appear bright blue, and the main river (low NIR, low temp) appears green.

         Further, shape and pattern are useful in identifying these features.  Agricultural lands are often regular polygons (rectangles, or otherwise straight-edged) and would be expected to appear more blue than red in my image, which is the case.  The river and forested island are obvious based on association.

Lab 8 – Surface Interpolation

An output from raster calculator determining the difference between
two output interpolation rasters using the same input points values

This week's lab involved using various interpolation techniques, generating surfaces, and learning limitations of each technique.  Each interpolation process determines the values of unknown cells based on their spatial relationship to known nearby values.  The difference between them is, generally, the weight given to each of the known values - which is usually based on distance and spatial trends.  At right is a comparison of two common interpolation methods: Inverse Distance Weighted interpolation and Spline interpolation.  The most obvious incongruities occurred in areas of low sample density, suggesting these two interpolation techniques duffer in predictions when sample density is low.

Module 7 Lab: Multispectral Analysis

 This weeks lab involved using multispectral analyses processes to locate specific features within aerial imagery. Generally four steps are followed: 1) examine histograms of the imagery pixel data; 2) visually examine grey scale image; 3) visually examine multispectral image; 4) use inquire cursor tool to isolate specific cell values. The following are three such examples of this process.  Features are highlighted by their contrast to background features.

Lab 7 – TINs and DEMs

Unmodified TIN

Modified TIN with lake feature burned in

 This week's lav investigated vector-based Triangular Irregular Networks (TINs) and compared them to the raster-based Digital Elevation Models (DEMs).  These are two elevation/topographic models used in GIS to produce accurate representations of real topographical features.  A major diffrence between the two models is that TINs use a series of triangles from several sample points (nodes) of known elevation.  To illustrate how a lake feature can be "burned" into a TIN model, the figures at right depict an original un-modified TIN and the resulting TIN.  Because the TIN uses points of known elevation, the border of the lake feature constitutes a series of points of known elevations.  Thus, the burned-in lake increases the number of triangles, both inside and out of the lake.  The slope inside the lake is 0 throughout.

Module 6 Lab: Spatial Enhancement

Image Enhancement output from ERDAS Imagine and ArcMap
Processing. 

This week's lab involved image enhancement using ERDAS imagine and ArcMap processing tools.  Raw aerial imagery often requires processing steps to produce an image that is spatially and radio-metrically correct.  Image processing software often performs automated enhancement to such images. ERDAS imagine and ArcMap were used to process an image (output seen at right).

Lab 6 – Location-Allocation Modeling

Output of new Market area coverage by distribution facilities

This weeks lab involved using Location Allocation solver in ArcMap.  Location allocation can be used to determine where to place a new store, fire station, or, in the project performed here, service area of a distribution center.  We were provided with an original market area map showing which distribution centers serviced which market areas.  The key is that the original distribution center service areas were generated organically, over-time.  Thus the true nature of our analysis was to determine which service areas should be changed.  The output from the location allocation analysis (see right) shows that several areas (13 total) were better served by a different distribution facility than the one originally selected for it.  Overall,  location allocation solver in ArcMap is a fundamental tool in large-scale data analysis, otherwise not logistically possible.

Lab 5 – Vehicle Routing Problem

This week's lab involved solving a Vehicle Routing Problem (VRP). VRPs are used here to determine optimal routes for fleets of delivery vehicles using a single depot.  Originally, the VRP solver was run with strict route zone restrictions generating an output where several orders were not filled, but profit was maximized.  A second scenario (show at right) was used to allow more flexibility in routing and generated a solution in which all orders were filled.  Customer service is important, thus the second scenario is the more desirable one.

Module 5a Lab: Intro to ERDAS Imagine and Digital Data 1

Final map output of landcover generated from
Landsat data first processed in ERDAS Imagine
and ultimately in ArcMap.

This week's lab introduced ERDAS Imagine image analysis software.  This software may be used to aid in the classification of land cover, as well as various other digital image processes and analyses.  We imported land cover raster data sets, manipulated and processed them in Imagine, and ultimately generated a final map output using ArcMap (see map to right).

Lab 4 – Building Networks

This weeks lab involved network analysis.  Routes were determined under a few different conditions including turn restrictions and predicted traffic.  Turn restrictions are relevant when dealing with one-way streets and large road medians.  Predicted traffic influences the time cost of traversing certain roads.  Ultimately, adding model inputs changes the optimal route; however, the significance of the change may be minimal.

Module 4: Ground Truthing and Accuracy Assessment

A map depicting accuracy proofing of LULC data of Pascagoula, MS. 

This lab involved truthing a Land Use Land Cover (LULC) classification map which was generated last week.  Ground truthing was done ex situ using Google StreetView of the area.  Much of the area was inaccessible from StreetView (western marshes) and were truthed using high resolution aerial imagery.

The overall accuracy is determined by comparing random points placed on the original LULC map to those same points found in Google StreetView or aerial imagery.  The overall accuracy of the LULC is determined by the number of "true" points divided by the total points.  The accuracy of my LULC map was 77%.

Lab 3 – Determining Quality of Road Networks

A comparison of "completeness" between the national TIGER data set
and a local, county-level, data set.

This week's assignment involved assessing completeness in road networks.  There are several means to assess the completeness of a road system; however, road length comparisons to known 'accurate' data sets are among the most common.  This involved calculating the total length of road segments within a grid of equal sized squares.

This was applied in lab by assessing the completeness, defined as road length, between a local and national road data set.  The map product at the right shows the comparison.

Lab 3: Land Use / Land Cover Classification Mapping

Above: Map of land cover land use classification of Pascagoula,
Mississippi using USGS LCLU Level II classification scheme.

This week's lab involved creating a land cover land use map of aerial photography using a standardized system.  The aerial photos encompassed portions of Pascagoula, MS and the system was the USGS LCLU system to Level II classification.  The map generated from this process is at right.

Lab 2: Determining Quality of Road Networks

This week's lab involved assessing the accuracy of road networks using a standardized protocol established by the National Standard for Spatial Data Accuracy (NSSDA). We compared the accuracy of two different data sets that represented Albuquerque, NM area.  The first was a local data set from the city of Albuquerque, the second was a national data set from StreetMaps USA (distributed by ESRI).  The process involved extracting intersections from both data sets and then digitizing the "true" location of the intersection using aerial imagery.  The distribution of intersections was quasi-random fairly thorough throughout the study area (see image at right of selected intersections).  It was expect, based on quick initial assessment, that the city data was more accurate than the national data set.  Our results showed this to be true:

Digitized features of the road network database from the city of Albuquerque tested 13.09 feet horizontal accuracy at the 95% confidence level using NSSDA testing procedures.

Digitized features of the road network database from StreetMaps USA tested 688.94 feet horizontal accuracy at the 95% confidence level using NSSDA testing procedures.

Module 2 Lab: Visual Interpretation of Aerial Images

This was my first module for Remote Sensing and Photo Interpretation course.  The focus of this lab was on identifying features within aerial images.  The first exercise involved locating areas of an aerial image that displayed different tones.  Tones, or brightness, may help the user identify features at those locations.  Next, texture, another good diagnostic tool for feature identification was exemplified.

In the second exercise, specific features were identified using size and shape, patterns, associations, and shadows.  Once a single object is positively identified (with a high degree of certainty) than it can be used as a reference in identifying other features.  For example, once you locate and identify a road, you can confirm the identity of a vehicle.  Then a building can be identied.  Because these aerial images have no scale to reference, within-image references are very useful.  A knowledge of the area (Pensacola BEach in map 2 at right) allows the user to determine water and pier features.

Lab 1: Calculating Metrics for Spatial Data Quality

This week was the first lab in Special Topics in GIS.  The focus was on accuracy and precision of data.  Accuracy and Precision are important aspects of all data, including spatial data, and it is important to communicate the accuracy and precision of original data to the receiving audience.

As part of the lab, we were provided an array of waypoints that were collected from a single geographic point using a Garmin GPS device.  We then had to calculate estimates of precision and accuracy after we were then given the "true" reference location.  Below are two outputs of these calculations:

Horizontal Precision (68%): 4.45 meters
Horizontal Accuracy = 3.23 meters

Horizontal precision, which is a metric of the clustering of repeated measurements of a data point, was calculated by determining the average distance of all the repeated measurements to the average of these measurements.  The accuracy was calculated as the difference between the average point from the repeated measurements and a given "true" reference point.

Module 11: Sharing Tools

This week's assignment involved properly packaging and sharing a tool.  This required setting correct parameters, relative file extensions, and providing metadata.  The tool generated an array of random points placed within a specified area, then created a buffer around each point (screenshot at right).  The Description for the tool was edited in ArcCatalog so that it was more user friendly, providing easy-to-follow parameter requirements.  The script from which the tool was based was then directly embedded into the tool and password protected.  Embedding the script in this way reduces the number of individual files that must be sent to another user for the tool to run properly, and it provides security (using password protection) against unwanted interceptors of the tool's script.

As this is our last module, below is a recap of the GIS Programming Course...

Employing the existing capabilities of a GIS, Python scripting can be used to complete otherwise time-intensive geoprocessing tasks.  This GIS Programming course provided a solid foundation for future work within the field.  We started with an introduction to pseudocode, a near-English version of a script that provides a workable rough draft for the author and a potential reference to another user.  We then explored similarities and differences between Python scripting and ModelBuilder in ArcMap.  Next, were several modules that involved writing and editing Python scripts to perform various tasks and produce desired outputs.   During these weeks, a level of comfort in working with Python was achieved.  Both raster and vector datasets were manipulated and scripting proficiency was quickly increasing.  Debugging and error checking procedures were followed in PythonWin so that script issues could be quickly discovered and fixed.  Finally, custom tools were created and shared.  Python is a fundamental tool in the GIS professional’s tool belt as it allows for complex analysis and timely output production.  This course was broad and deep in content and thorough in delivery – a great experience overall.

Module 10: Creating Custom Tools

Custom Tool window

Script Message output

 This week's assignment involved creating a new geoprocessing tool in ArcMap using Python scripting.  Creating custom tools is an important aspect of GIS Programming because time-intensive geoprocessing operations may be completed in a quick, automated manner.  The general steps of creating a custom tool are as follows:

1.       Generate a script that contains unspecified input and output variables and will complete a process of interest

2.       Create a new toolbox in ArcMap using the Add > Toolbox dropdown in ArcCatalog

3.       Add the script (the script generated in step 1) to the toolbox

4.       Set the parameter options for the script/tool

5.       Connect the input parameters of the tool to the input and output variables in the script

6.       Add messages to check for progress of the tool and for debugging issues.

7.       Check to see if output is as desired, if not then debug script

Participation Assignment #2: Migration Patterns of Northern Saw-Whet Owls

 Beckett, Sean R. and Glen A. Proudfoot. 2011. Large-scale Movement and Migration of Northen Saw-Whet Owls in Eastern North America.  The Wilson Journal of Ornithology 123(3):521-535.

In this paper, researchers mine a bird banding database to tease apart patterns of migration and large scale movement for the Northern Saw-whet Owl (NSWO) in eastern North America.  The United States Geological Survey (USGS) oversees several bird banding stations in the United States and manages the large database produced from them.  Bird banding stations are permanent, semi-permanent, or temporary field locations where ornithologists capture target bird species and place on them leg bands engraved with unique serial numbers.  The overall number of birds caught at specific banding stations may fluctuate seasonally or annually; thus, population trends or mass movement patterns may be detected using relevant spatiotemporal analyses.  Further, birds with bands can be recaptured within or between years so that individual movement and migration routes can be analyzed.

The Northern Saw-Whet Owl is a small owl that resides in the United States.  This study attempts to use banding records of this bird to determine if it migrates southward during the fall season, if individuals follow similar migration routes between years, and whether there is any age-related differences in movement.  They use a GIS (ArcView 9.3; ESRI 2008) to complete these analyses.

They examine the number of banded NSWOs using of 01° latitudinal lines as groups.  They find that the peak banding day – i.e. the mean Julian day – occurs later in the year as your move further south.  Thus they determined that NSWOs have a mass migration south during the fall months (fig. 3, Beckett & Proudfoot 2011).  Then, by creating vectors from original owl capture to recapture locations, they generated a rose diagram depicting the southerly movement for the birds during fall (fig. 5, Beckett & Proudfoot 2011).

This paper is significant in that it answers a large-scale question utilizing a public-access database – in other words the data was gathered, not collect, by the researchers.  They illustrate the use of databases and GISs as a foundational tool for understanding ecology of animal populations.  With increasing technological advances and utilization, similar research will continue to be produced.

link to paper: http://www.projectowlnet.org/wp-content/uploads/2011/09/Beckett-and-Proudfoot-2011-Wilson.pdf

Module 9: Debugging & Error Handling

Output for Script 1

Output for Script 2

Output for Script 3

This weeks lab involved debugging and error handling, perhaps the most frustrating part of writing scripts in Python.  There are three types of errors encounter in Python: syntax errors, exceptions, and logic errors.  They can arise from improper character entry into the program, or from errors in geoprocessing.

The assignment involved debugging three scripts in various ways, creating the outputs seen at right.  PythonWin contains its own debugging interface, which was used to help the user locate or trap exceptions.  In the Output for Script 3 (below), an error message is presented, but the script was allowed to continue. Essentially, that portion/block of script was passed over.

Module 8: Working with Rasters

Final raster output that meets all criteria.

This week's lab involved working with rasters using the spatial analyst module in arcpy package.  Rasters allow for the display of continuous spatial phenomena and analysis using a GIS has made complex processes relatively simple.  The final lab output was a raster meeting three specific criteria.  These were: a specific landcover type, a slope between 5 - 20 degrees, and an aspect between 150 - 270 degrees.  The script written uses only DEM and landcover inputs.  At rights is a final raster output, below is a discussion of an issue that I encountered during the lab and how it was resolved.

1.     The only issue I had was that my final map displayed a raster with values of 1, 0, and NoData and thus looked different than the example shown on the assignment PDF.  Immediately I recognized that this was likely due to the landcover reclassification step.  The original script was as follows:

>>> outreclass = Reclassify("landcover", "VALUE", myremap, “NoData”)

2.       I investigated the Reclassify tool’s syntax through ArcMap help > Reclassify tool.

3.       “NoData” was displaying differently because I had specified it to do so in the script.  The final argument of how to represent NoData was optional, but I had entered it in.  The original script was copied from the Mod8 Exercise, this was the root of the problem.  In the Mod8 Exercise we purposely wanted to specify cells with NoData; however, in the Mod8 Assignment, we want to specify all cells that do not meet the criteria with a value of 0.

4.       Therefore, I removed the last, optional argument in the original script, creating the following final script, which ultimately produced the correct final raster:

>>> outreclass = Reclassify("landcover", "VALUE", myremap)

Week 9: Corridor Analysis

Corridor model

This week's assignment involved creating cost paths and corridors.  Ultimately, a model for black bear movement between two national park units was created.  Cost paths and corridors are used to predict the pathways that are least costly to construct (in the case of roads and utilities, etc.) or traverse (in the case of wildlife, utilities, etc).

The corridor output at right shows a predicted black bear corridor.  The parameters used to create this corridor were elevation, land use (not shown), and roads (not shown).  Bears prefer certain elevations, certain habitats, and avoid roads.  So a suitability analysis was performed to find the most bear-friendly areas.  Then the corridor analysis was use to create the best path between the two units.

Module 7: Working with Geometries

Screenshot of rivers_PCoppola.txt file

This week's lab focused on the conceptual and tangible connection between geometric/vector objects and the attribute table.  Specifically, we worked with features, arrays, and vertices by using cursors and tokens to produce a .txt file of select items from the attribute table (see screenshot at right).  The feature was a system of rivers (polyline feature class) on Hawaii.  We wrote a script that would iterate through all the arrays of this feature class and provide an output of the feature number, vertex number, x,y-coordinates, and feature name.  This required nested for loops to iterate over the embedded arrays.

Week 8: Network Analysis

This week's assignment dealt with network analysis.  At right is a service area analysis depicting the change of catchment coverage for Austin Community College after the closure of the Cypress Creek Campus.  The service area consists of several layered areas within a specified range.  It is clear that coverage changes, and the ultimate effect of this change was further assessed using demographic data.  For example, over 1,000 college student aged citizens who were closest the the closing campus must now attend a different campus, thus increasing the student-averaged travel time to any campus.

Week 7: Suitability Analysis

Final map product outputs

This week's assignment involved applying suitability analyses using several criteria and different methods.  Both vector and raster methods were applied for a straight-forward 'binary-type' suitability analysis, then a weighted overlay analysis was performed.  Ultimately a comparison was made between two different weight schemes using the overlay method, and the final outputs were compared (see map product at right).

First, a raster grid displaying a landscape of suitability for each criterion was made.  Generally, places on low slopes, far from rivers, close to roads, on particular soils, and on particular land cover types were considered more suitable.  Then, the five criterion themselves were weighted.  The map compares the equally weighted criteria output, and the unequally weighted criteria output.  In real applications, weight assignment is determined by accepted research, personal observation, or popular opinion within the decision-making community.  Clearly in the map above, slope has a large influence on the suitability of particular areas.

Module 6: Exploring & Manipulating Spatial Data

Python Script Output

This weeks assignment was to dive deeper into Python scripting by manipulating lists, dictionaries, tuples, and using cursors.  List, dictionaries, and tuples are similar in that they store indexed data, however there are nuances to each that allow for diverse usage.  Spatial data is often stored in some sort of indexed format (tables) which must be easy to reference and manipulate.  The assignment here involved generating a geodatabase and editing some fields of a feature class within.

At right is a screenshot of my script output, which was used to select only county seats from a list of cities and then match each county seat with its population using a dictionary.  Below is a discussion of some issues I had during write-up and how they were overcome.

I had the most trouble with Step 5, which involved setting the search cursor to retrieve three fields while using an SQL query to only select County Seat features.  The issues occurred in three places of this step:  1) Setting the workspace; 2) calling the correct feature class; and 3) determining the syntax of the Search Cursor.

The first issue was merely an unnoticed typo, but it cause a lot of troubleshooting because I thought other parts of this step were causing the error message.  Typos are small errors that can cause big headaches.  Once realized this, it was quickly fixed and I moved on.

The second issue was a result of not knowing what file extension to use for the feature class cities in the new geodatabase.  I first used .shp, but this gave me an error saying that the file could not be located.  I then used windows explorer to determine if the extension had changed, and I found that it had changed to .spx.  Therefore I tried calling the feature class using this extension.   This again gave me the same error message.  I then thought that perhaps the basename extraction step (i.e. removal of .shp during copying step), produced a file with no extension, therefore I tried simply using ‘cities’, and this worked.  I then moved on to issue three.

Issue three was the toughest because I wasn't sure whether to use brackets, parentheses, quotation marks, commas, semicolons, etc. when using the Search Cursor.  Essentially the amount of info that was covered this week left me a little confused.  I realized, however, that I was over-thinking the problem.  I knew that the three fields needed to be within brackets, separated by commas, and within quotes.  Then I referred back to the exercise to determine how to use the SQL query, which was straightforward.

Week 6: Visibility Analysis

Finish Line Camera Coverage Map (symbology described in text)

This weeks lab involved using Visibility analyses on topographic data.  Specifically, we utilized Line of Sight, Visibility, and Viewshed tools (among others) in the 3D Analyst toolbox.  We worked with DEMs to determine what regions were visible from both point and line features and compared the outputs of these tools.

We also used veiwshed analysis to make an informed decisions about surveillance camera placement around the finish line of the Boston Marathon.  We were restricted to a 90 degree field of view for our cameras, and there were several buildings which obstructed possible placement.  I first analyzed aerial imagery to determine building locations, as I wanted to place the cameras on top of a tall structure.  I then used a DEM which included building height to determine how high these cameras would be placed.  The viewshed tool produced a raster with cell values from 0 - 3, where the value represented the number of cameras that captured that particular cell.  Cells with a value of 3 were colored red, 2 were yellow, and 1 were green (zeros were omitted).  Above is my output using this symbology.  [The cameras are magenta triangles and the finish line a blue dot].  All three of the cameras had an unobstructed view of the finish line as well as much of the surrounding area.

Module 5: Geoprocessing Using Python

This weeks assignment was to become familiar with geoprocessing in Python.  This was primarily accomplished by writing scripts that performed sequential geoprocessing tools in the arcpy site package.  The following is a breif transcript of my notes to complete the assignment.
1. First, I looked in the help window for a description on the Add XY tool as well as the Dissolve tool, just to get an idea of how they worked and what they did.
2. Next, I opened the ArcMap Interactive Python window as well as the PythonWin program.  I first used the scripts in ArcMap so that I could visually see what was happening to the data, then copied the text to a new script file in PythonWin.
3. Because the script should be standalone (outside of Arcmap), it first required to import the arcpy site package.  Then, I imported environment class and set the overwrite option to ‘true’.
4. Then I used the three tools in sequence: Add XY, buffer, and dissolve.  I followed the syntax described in the help folder as well as the easy to follow interactive python help window in ArcMap.
5. After each tool I added the GetMessage function.  Further I used the  ‘+ “\n”’ after the first two tools so that the message was easier to read.
6. I tested the tool in PythonWin and ArcMap to see if the desired result occurred; it did.

Week 5: Crime Hotspot Analysis

This week's lab involved creating several different Crime Hotspot Maps and comparing them in their predictive power.  We created hotspots using Local Moran's I, Kernel Density, and Grid Overlay from 2007 burglary data.  Then we compared the number of 2008 burglaries within these 2007 hotspots.

 I argue that the best crime hotspot predictive method in this scenario is using Kernel Density.  I base this on the observed highest crime density of 2008 burglaries within the 2007 hotspots. This is category (above) that I view as the best metric of predictive power because it takes into account hotspot total area, and thus would provide efficient preventative resource allocation.

It is clear that the Grid Overlay hotspot contains the most 2008 burglaries, therefore it is certainly a suitable predictive tool.  However, the total area is >65km² and may be too large to effectively patrol by police.  With unlimited resources (i.e. police officers/vehicles), it would be feasible to use this model as area to patrol. In a limited resource situation – which is what is most often the case – higher priority areas must receive preferential resource allocation.  Thus, the Kernel hotspots.

The Local Moran’s I hotspots are large, but contain the fewest amount of 2008 burglaries.  The large area appears to be a result of extreme density regions.  By visual assessment, it appears that areas between several clusters, which don’t look particularly dense themselves, are influenced by adjacency (see screenshot below).  It appears that the red area toward the top is in between two hotspots.

Module 4: Python Fundamentals - Part 2

This weeks assignment involved debugging an existing script as well as writing our own to produce a desired outcome.  A script that was given to us was supposed to produce a simple dice rolling game between several participants, but it had several errors in need of correction.  To fix these errors, the debugging toolbar was used and error messages were interpreted.  Eventually the output was produces as seen above.

We were then required to randomly generate a list of 20 integers between 0-10 and remove a specific 'unlucky' number.  I chose the number 6 as my “unlucky” number, which was removed.  To print the number of times that 6 appeared in my original list, I used the if-elif-else structure.  Using the count method to determine the number of 6’s in the list, I chose the following three conditional outputs:

If there were no sixes, then it would print “This list contains no sixes.”  If there was one six, then it would print “This list contains 1 six, which was removed.”  If there were multiple sixes, then it would print “This list contains (the number of sixes), which were removed.  

I used the while loop and the remove method to remove each six sequentially from the list until the list contained no sixes.  Then the new list with all sixes removed would be printed.

Week 4: Damage Assessment

Damage Assessment of structure on East New Jersey
coast after Hurricane Sandy

This weeks lab involved damage assessment of structures near the New Jersey Coast after Hurricane (super storm) Sandy in Fall 2012.  Damaged structures in this case were homes and businesses that were inundated or wind-ravaged.  We compared pre- and post-Sandy aerial imagery of our study area and visually determined the severity of damage (see image top-right; description below).  Then, we regressed the severity of structure damage on the distance from the coastline in intervals of 100 meters (e.g., 0-100m from coastline, 100-200m, etc.; see table below).

I first coarsely assessed the entire affected area using pre- an post-Sandy aerial imagery.  This visual assessment included areas outside the delineated study area that were highly affected, as well as areas that appeared almost entirely unaffected.  This was done to make my damage assessment more objective.  Based on this broad-scale approach, I determined that the study area was one in which structure damage was highly variable – some properties were destroyed completely, while some looked unharmed.  Further a majority of destroyed structures were located in the easternmost region of the study area.

The general process of identifying the structural damage for each parcel was a left to right sweep of each block.  This allowed sufficient detail without taking hours to process a small area.  I primarily used the ‘slide’ effect tool to compare the two aerial images of the pre- and post-Sandy study site. If a structure was clearly moved from its original foundation or was leveled, then I chose to label it as destroyed.  Some structures were absent all together, those were labeled as ‘destroyed’ as well.  If a structure had major collapse, which was noticeable from aerial imagery by debris or a change in shape, then it was labeled ‘major damage’.  'Minor damage' was subjectively decided to describe a home that was generally surrounded by other severely affected homes, but was not obviously damaged from imagery.  ‘Affected’ was given to any home that appeared to have debris in the yard, which I presumed to be material from the structure.  A structure was labeled ‘no damage’ if it appeared generally identical in shape to the pre-Sandy imagery, and was surrounded by other homes which appeared unharmed.  This was based on the assumption that adjacent homes protected those upwind and uphill of the storm.

Structural Damage Category	Count of structures within distance category
	0 – 100 m	101 – 200 m	201 – 300 m
No Damage	0	1	7
Affected	0	9	24
Minor Damage	0	16	6
Major Damage	0	8	4
Destroyed	12	6	4
Total	12	40	45