Monday, June 26, 2017

DC Crime

Module five looks at the GIS applications in crime fighting and prevention, specifically offenses such as burglaries, homicides and sex abuse occurring in the nation's capitol.  My completed maps are below:

The first shows the overall crime occurrences to police stations.  The police stations are effectively placed throughout the city in order to handle the crimes happening in each area as seen in the first analysis.  Also in the first analysis there is a chart of all offenses and their frequency.  The second composite map shows the occurrences of three different crimes and the relationship to the police stations.  This map was created using the kernel density tool and a selection by attribute tool for the specific crime.  Burglaries are widespread and frequent whereas homicides and sex abuse crimes are less frequent.  My proposed location for an additional police station would be in relation to the burglaries map given the frequency and widespread nature.

I enjoyed this lab, particularly the kernel density tool.  I also enjoyed the geocoding of the police station addresses.  The crimes matched up with the police stations after the geocoding was completed by picking the one unmatched address from the map using Google Earth.

Wednesday, June 21, 2017

Geoprocessing in ArcGIS

I found this module, Geoprocessing in ArcGIS, to be quite helpful in bringing together many concepts and using multiple programs (ArcMap, Catalog and PythonWin) in conjunction with one another.  Screenshots are included below showing my final model that clipped the soil layer to the basin layer, selected out the 'not pristine farmland' areas and erased them.  There is also a screenshot of the output added to the map.

I enjoyed using the Model Builder as it is most intuitive and the visual is helpful to someone who has had very little programming experience.  Going through all the various ways to find and use the different tools and methods.  It was also quite useful to learn the advantages of exporting the models, built in Model Builder, to a python script and also to learn how to use the scripts across different platforms.  In the model builder it is easier to discern any errors due to the fact that the various blocks of tools and variables change into filled blocks of color when they are ready to run whereas they are hallow if parameters/inputs need to be set or edited.  Working with the script created in Model Builder in PhythonWin required more complete filepaths and also an input to set the workspace environment.

This deliverables for this module were the python script that clipped the soil layer and erased the 'not pristine farmland' and the created toolbox.  These were sent to a zipped folder in order to submit.

I enjoyed this lab and found it the most helpful so far in this course.

Sunday, June 18, 2017


This week's module and lab focused on Hurricane Sandy which struck the New England coastal areas in 2012.  The first part of the lab was to create the hurricane tract and in so doing provide the overall picture for the second portion of the lab which involved assessing damage.  The first map is seen below:

This map was fun to create and it felt like doing a real world project, which is true.  I enjoyed customizing map symbology and using it to show where this monster storm affected.  It was also a first in using the graticules and having a slightly skewed north arrow.  In the second portion of the lab we focus on the New Jersey coastal area of Ocean County, one of the harder hit areas as well as a FEMA Disaster Declaration area.  In examining the aerial imagery for this section of the lab the reality of people's lives being changed did hit home.  My finished damage assessment map is seen here:

Various tools were used to organize data, particularly the data mosaic tool.  This made it possible to add various levels of data at once and so analyze the imagery side by side by using tools such as "swipe" and "flicker".  These tools were quite useful in determining the damage caused.  Next I used the editor to define various parcels according to level of damage sustained, this was done by analyzing the pre and post storm imagery.

I enjoyed this lab and find it useful in the real world.  The far-reaching capabilities of GIS will continue to make significant impacts on storm affected areas and the people who live there.

Thursday, June 15, 2017


This week's exercise and assignment in the Programming course focused on Debugging.  This was by far the most difficult assignment for me thus far (several factors) but it was not a total learning loss. Screenshots of my scripts are below.

Obviously, none of these scripts were successful despite many attempts and strategies.  I did have trouble but understood the theory of this week's lesson.  The error messages provide the location of the error by line number and the nature of the error.  The debugger can be used to "check" the code line by line.  This is helpful in that edits may also be made along the way.  This was helpful in reassuring that lines are correct or not before proceeding.  I also found the "try-except" statements to be most useful.  These allow the code to run despite errors by providing an alternative, of sorts, to the code.  There are many tools at the disposal of anyone writing code within the PythonWin environment.  This is encouraging!

My article for the participation assignment this week focused on how GIS is being used to educate people about climate change.  The article can be found following this link:

The article examines a pertinent issue in the world today, climate change.  The article focuses on using maps, created using GIS, to illustrate the effects of humans on the environment.  It also makes the argument that the visual proof resonates more with people.  We hear the term "climate change" so often that sometimes the visual can have a greater impact on us and hopefully spur us to action.  With the influence of the internet, these types of maps are readily available.  Through platforms such as Google Earth anybody with a computer and an internet connection can examine past and present satellite images and see the changes that humans are perpetuating at alarming rates.  The article exhibits temperature maps, satellite image maps, and maps depicting ice melt, forest fire and sea level changes.

Monday, June 12, 2017

Tsunami Evacuation

Module 3 of the GIS Applications course focused on creating maps for use in a Tsunami natural disaster and coordinating evacuations should the event occur.  Some data was created in ArcCatalog, some in ArcMap and some in the Model Builder.  My finished product is seen below:

For me this was not a simple lab but I enjoyed the subject and the real world applications it implies as well as the use of many different types of data both given and created.  This map gives both the evacuation zones relating to radiation exposure threat and runup/water inundation threat.  Various cities are shown as they relate to the various evacuation zones.  Many different types of data were created to create the evacuation zones, primarily DEM or elevation rasters.  These were used to determine the flow of water due to elevation which resulted in the three zones depicted in the smaller inset map of the coastline at Fukushima.  The Model Builder was used to create the runup evacuation zones.  This visual method of coding was most helpful in using many tools and interpreting their outputs and how they related to the purpose of this map.  The Model Builder is most useful to me as it changes color when it is "correct" and ready to run; I enjoy this intuitive approach.

This lab was time consuming and each part relied on others.  This made for many opportunities to go back and redo, however, it was a good learning experience and I enjoy the end result.  The map is useful and pertinent to our times which I find most important.

Wednesday, June 7, 2017

Intro to Python Part 2

Module 3 in the GIS Programming class continues with part two of the Introduction to Python which gives a good foundation for all that will follow in this course.  The exercise and assignment this week focused largely on conditional statements and loops.  I experienced a never-ending loop, or four, during the process of working through the assignment.  Though a good deal of frustration resulted, I was able to finally complete a code that ran and produced a result, though not perfect.  A screenshot of my final code is seen here:

The code begins with a dice game of sorts in which names are included in a list and the "roll of the dice" corresponds to the length of each name.  Various conditions, created in a conditional statement and loop, are met or not and corresponding responses are given: "Mary Wins!", "Claudia Loses!".  The second section of this code generates a random list of numbers, between the values of 1 and 10.  The tricky part was to insert 20 random numbers between these same values.  This was again achieved with a for loop statement.  The final section seeks to eliminate an "unlucky" number from this list and to print a statement in its place should this number not be randomly generated.  This was done with an if statement and loop.  The trickier part was to eliminate the number from the list.

I enjoyed this lab as it brought many of the concepts from last week into use again while introducing new ones to use in conjunction with the previous.  Though slightly overwhelming to one who has very little actual experience with any kind of coding the exercise and assignment were both helpful in increasing my understanding of this complex topic.

Monday, June 5, 2017

Lahars Hazard Planning

This week's lab in GIS Applications was, to me, the most difficult so far in the GIS Certificate.  This was due largely to technical difficulties encountered.  Though the material is intuitive, getting the tools in ArcMap to work correctly nearly got the best of me.  The lab examined the dangers of a Lahar event around Mt. Hood and found the schools and populations that would be at risk during such an occurrence.  The final result was created entirely in ArcMap and is seen below:

Mt. Hood is seen towards the center of the map/study area.  All layers have been clipped to this study area to reduce clutter and represent the areas of concern in greater detail.  Various spatial analysis tools were used throughout this lab to narrow down the features of the area in order to only show the hydrology that was relevant to a Lahar event.  The Flow Accumulation tool was to produce a result that showed where water would go depending on the pixels and their representative distance from Mt. Hood.  This process never successfully completed for me despite many many hours of trying over several days.  Eventually I had to make a final stop of the tool, exit the program and go back in.  The layer was then visible under "add data" but it never completed successfully onscreen.  This severely hampered the processes past this step.  The CON tool was also problematic and took many tries to produce a usable result.

The Raster Math section was helpful in making sense of the numbers.  An Excel table was joined to the Block layer to obtain population numbers in the buffered area, one-half mile, around the streams and rivers that were determined to be hazards.  Combining the numbers and tools which produce a model of potential results was quite helpful in visualizing the real world applications of GIS, particularly as it relates to hazard planning.  Despite major setbacks with the tool processes, I did learn quite a lot in this lab and enjoyed creating something which could be of real purpose to people at risk of hazardous events.