Blog#11 Coordinate Systems

When it comes to comparing and contrasting the project and define projection tools, there are a couple of things to keep in mind.  Recalling what we have read in the text and the lectures, projecting refers to taking the three dimensional elements of a map and converting them in such a way that the data on the map is useful in a two dimensional format.  The define projection tool is the first step for anybody who is plotting points on a map.  For these points to be accurately reflected, the map must be set up along the proper coordinate system.  The define projection tool accomplishes this by configuring the map.  The project tool applies to the points that are to be placed on the map.

My answer to the second part of our blog question of the evening concerning the procurement of cs projection data in the event the information is not available on the source website focuses on a couple of different options one has at their disposal if this problems occurs.  Something I would suggest trying is locating data with similar properties to that which lacks the projection information.  Similar data might have this information and at least give you a stating point in terms of what to try first when defining your data.  In this process I would also check to see if maybe the projection information is available through a secondary source or website.



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blog #10 locating census data

The historical county data for Ohio was not too terribly difficult to find.  I located it at the bottom of the state quicklinks page for Ohio.  While many of the pages allow for downloading tables and maps that can be imported into ArcGIS with few if any changes.  The data for the historical county populations is a.txt file and might require some formatting changes to be made in order to make proper use of the statistics in the GIS program.  I will say part of the reason the data on the census website was not too challenging to find for me is the fact that I have had to use this site in a number of previous classes for data tables and maps.  If I were new to the site for the first time, I could see someone having some problems finding what they are looking for depending on how specific the data is they are looking for.  Here is the link to the historical county data:


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Blog#9 Geocoding rematches

There are a number of challenges one faces when dealing with unmatched addresses in a geocoding batch.  The types of errors that potentially exist in a given dataset can include misspelled roads, incorrect address numbers, directional issues and differences in types of roadways.  Misspellings are usually isolated and not present throughout a dataset whereas if there is a difference in road types or directional prefix or suffix, these types of problems are more likely to be present throughout the data. Therefore, mistakes can potentially exist throughout or be unique to an individual address.  Even after numerous efforts, it is sometimes simply impossible to find matches for every entry in a dataset.  The goal of the rematch process is to reduce the number of unmatched entries as much as possible.  If this occurs and one still has unmatched entries, it may be necessary to take a deeper look at the unmatched entries to determine why no candidate can be found.



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Blog #8 Free GIS Data

In my opinion, lab #8 was a welcome change of pace.  The opportunity to work with external data and use it to produce a relevant map in ArcGIS was very productive.  Over the previous couple of labs we have gotten to use more of the assets of the toolbox feature.  From a tactical perspective I would say this lab like most of the others was not terribly difficult as far as performing the techniques necessary to produce a given result for a map.  The challenging aspect for me continues to be being able to better decipher what functions work best in which situations when assessing data.  As far as the confidence thing goes, I do feel a little bit better in terms of being able to perform most of the tasks set forth in the labs.  I really didn’t have much of a problem obtaining the data from the seamless server and I found the video demo quite helpful in this regard.  Overall, I would say my experience with lab #8 was good and not quite as stressful as some of the previous labs have been and for that I am quite relieved.




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Blog#7 Class thus far

As one might expect with a condensed summer session class, the lessons and coursework feel like they have flown by.  The way I see it, this has produced some positive outcomes as well as some negative ones.  On the positive side, I have gotten the opportunity to see how data in a GIS environment can be used to make useful and informative maps that can be applied to a variety of professions such as community planning, resource management, and land preservation just to name a few.  On the negative side, the pace of the class and the time constraints that come with it have made it hard to sit back and firmly gauge what I’ve learned so far.  If my job schedule allowed for it, I think I would have really enjoyed taking this course in a full semester format in order to better absorb everything we have covered so far and will cover in the remaining time we have left.

If there is one bit of information or advice so far that would aid in the learning process it is that sometimes during the lab exercises, the book is a little vague as to what it wants you to do and what data to use for it.  The concepts tutorials take a considerable amount of time to walk through and I think if there is one thing the book could do better it is more clearly linking what you learned in the tutorial with what you are attempting to apply in the exercises.  I’m a learner through repetition so once I can establish a pattern in a given question it is much easier to perform the necessary tasks to get the right answer.  As we get closer to the end of the class, remembering the rules that apply to joins and other data processes will be important to keep in mind.  Here’s hoping for a strong finish to the class!!

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As explained in the text, distance joins really only work when dealing with projected coordinate systems as opposed to a GCS.  The information supplied via GCS is too complex to easily obtain accurate and practical distance information.  As for the lab exercises, like many of my peers I encountered a few challenges working through the problems.  One problem I have had at times is the feeling that maybe I’m not pulling the right data layers out to be used.  There are a number of different categories in the different datasets and some of them are quite close to each other in their respective properties.  As long as I have the right data in front of me I feel like I am getting the hang of the data analysis side of things.  Now if only I could improve my artistic touch on my mapping skills I would really be in business lol.



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Blog#5: Query Syntax

A. [ZONE] = “COM” AND [ZONE] = “RES”.  This appears to be the correct syntax for this query to select commercial and residential zoning areas.

B. [COVTYPE] = “SPRUCE” AND [CROWNCOV] > 50.  Incorrect.  The expressions indicate different data types that cant be compared.

C. [POP2000]>2000 OR [POP2000]< 9000. Incorrect.  Assuming the purpose of this query is to return data with a population range.  the operator and should be used instead of or.

D. [INCOME] < 100000 AND [INCOME]> 50000.  Correct syntax but would probably look better if the the number sequence was flipped to create a range of 50000-100000 instead of 100000-50000.



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Blog#3: good map/bad map

For my examples of good and bad maps, I decided to stick with maps displaying data for the United States.  The good map focuses on religious practice while the bad map shows information about the different words regions use to identify soft drinks.

The Good Map:


I realize this map might not look as clear here as when I found it online.  This map appealed to me on the most fundamental level we have for evaluating a map.   It presents notable data in a form that is intelligible and gives the viewer a better understanding of religious patterns in the US.  The data is broken down into four classes and the color scheme is not over the top yet easily distinguishes the classes from each other.  My examination of the map finds it to largely be in compliance with each of the Six Commandments of Krygier. The improvements I would suggest can be made to this map involve T7 and T8 as some of the specific entries dealing with the source of the data is a little small and hard to read.  To the average viewer of this map, I think it is fairly easy to decipher that religious adherents constitute a larger proportion of the respective populations of states in the middle of the US while the east and west coast populations are less concentrated.

The Good:

1.) Good color selection

2.) Easily decipherable data classes

3.) Clean, not cluttered

4.) Good presentation of data

The Bad:

1.) Labeling is too small and uses poor coloring.

2.) Data is a little old (11 years)

3.) Map has some dead space to it.

4.) Data description is lacking.


The Bad Map:


While the gif format of the good map ironically makes it a little hard to read in the blog, my choice of a bad map looks a little better in terms of reading it, but the there are problems I have with this map that in my view makes it in need of improvement.  The data in this map measures the prominence of people using terms like pop, soda, and coke to refer to soft drinks. The principle problem I have with this map is that the data classes are broken down further into four subclasses.  This creates an image like the one above where there is a little too much variety going on.  Some general trends can be observed such as the notion that pop is popular across much of the north while coke dominates the south.  However, I think that overall this map lacks in areas of graphic excellency covered by T2 and T6.  The subclasses and the variation of colors used can make it difficult to determine which data class you are looking at.

The good:

1.) png format projects better than gif of the good map.

2.) Like the good map, the presentation is clean and clutter free

3.) Data subject is interesting

4.) Labels are easy to read.

The bad:

1.) too many color variations.

2.) age of the data (8years)

3.) Difficult to interpret information as displayed.

4.) Data is rather ambiguous. Other is used as a variable.


links to maps:





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The difference between layer files and feature classes

As the text points out, there is one significant difference that separates layers from feature classes.  Feature classes is used to refer more generally about data with a particular dataset whereas a layer applies to taking that data and projecting it on to the map surface.  Lab number 2 provided the opportunity to work extensively with layers by moving data around to interact with other feature classes.  As the course progresses and the GIS software becomes more familiar, the techniques we are learning now will no doubt be used to identify relationships between data and the geographical environment in which it exists.


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GIS Blog #1

Two of the more interesting features I come across while learning my way around ArcGIS is the ability to move data around between the Catalog and Map programs in a simple and straightforward fashion.  While I haven’t at this point tested much in ways of the geoprocessing options, I am sure the experience with that will come in the ensuing chapters and exercises.  It also appears that the software is quite capable of blending new current world data with established data files to produce new data analysis that is easily customizable.

ESRI website

As for the GIS website, I think it serves as an important tool for a couple of reasons.  First, it provides interactive and expansive supplemental inputs for ArcGIS that allow you to do more with your data than you could without online access.  Second, the website provides a wealth of information concerning different applications of GIS technology in the real world which is helpful in showing how what we learn through the course can be used outside the classroom.



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