Week #6 Learning Journal "Using GIS in Business"
These are just examples from the previous class not for this week class to know how the takeaways look like.
Example 1:
After watching the video “Hans Rosling’s 200 Countries, 200 Years, 4 Minutes” I got a much better understanding of different countries’ life expectancies and income. The use of Rosling’s graph was extremely helpful at explaining the life expectancy of each country through the years. It was amazing to see how as time went on countries like Europe and America strived at improving health and increasing income, while other countries like Africa and Asia remained on the low end of the graph. It was impressive how he was able to visually document the effects of WW1 and health epidemics on certain populations. It was also interesting to see how the poorer and sicker countries started to get healthier once they gained independence; Rosling’s graph really demonstrates how destructive epidemics and civil wars are on developing countries.
I was really intrigued when Rosling took the country of China and split it up based on provinces; we were able to see that developed countries were at the same level of wealth and health as Italy, while the rural areas were at a low quality of life compatible to Ghana. This reminded me of when I went to Thailand last summer. In the main city of Bangkok (where all the wealth was) you could just see that the people were well fed, has access to health care, had paying jobs, it was just another very well developed city. However, when we ventured out to the more rural cities like Kanchanaburi, you could see that the streets were dirtier, there were more stray animals, it was obvious which towns had money and access to healthcare and which didn’t.
Looking at the “map of the week” we’re able to see a breakdown of each city in America based on factors like population, birth and death rates, weather, and crime rates. We were even able to see a presidential breakdown of cities for this past election. I was blown away by the amount of data that this website listed. I looked up my hometown Napa and was able to see a description of all these different features over the past decade. I also looked up cities that I’m going to be applying to for graduate school; it was really cool being able to see people’s average income, the median rent prices, number of attractions and restaurants, and common occupations. I’m going to be going to grad school for a Master’s in Nutrition, so I was really excited to see a breakdown of the average health and nutrition for the city and its corresponding state. I will definitely use this website again when I get close to applying to grad schools. Being able to utilize this type of information and data will definitely play a significant role for what city I decide to move to.
After reading “Localization: Not Just Location”, a short e-book written by ESRI, I started to think about local businesses here in Ashland. In this e-book the author discusses the importance of localization and the role that it plays in effectively and efficiently running a business. According to this article localization is not just about finding the right location for a new business, but also making sure that there are customers in that area willing to come. It is important to know customer spending habits before opening and operating a new business. “Purchasing behavior and shopper frequency are driven by convenience; organizations need to capture and understand shopping habits, not just buying habits. It’s no longer acceptable to use the distance from a store as a model of changes in sales potential or increased competition.” This quote had me thinking about the vast number of coffee shops here in Ashland, especially in the downtown area. With all these shops are in close proximity of each other, it is important for the owners to design their shops so that they appeal to the demographics in the area that they are catering to. Not only this, but with competition so close, these coffee shop owners want to take advantage of localization and make sure they understand the market opportunity and customer spending habits so that they can become profitable once open.
I also read “Revealing the ‘Where’ of Business Intelligence using Location Analytics” which was also published by ESRI. This article described how different types of businesses use Business Intelligence (BI) which helps businesses better use and analyze their data. Some of the features of BI include being able to use maps to gather more data. According to two thirds of the respondents use maps to assess management, plan real estate, and conduct risk management. As I read, I could see that bias that this “white paper” and see how it was trying to get, me the reader, to think a certain way or perceive on thing better than another. It was as if this article was advocating that using GIS and BI are the only ways to excel with a business; businesses want to use this type of software to be able to capitalize on the provided opportunities.
Example 2:
The article titled, "Revealing the 'Where' of Business Intellegence...," made the point that many businesses do not understand fully the scope of what BI location analytics can do. I was surprised that over 50% of respondents stated that BI location analytics was "nice to have." When I think of something that is "nice to have," I think of things like my daily coffee or a reserved parking space. Things that add enjoyment and convenience to my life but I can most certainly live without. These businesses that think GIS is "nice to have" are missing out on so much valuable information. I would bet that with the experience I have now from this class and BA 497, I could create a data visualization map that would change the minds of the "nice to have's."
In the e-Book that Esri published, they talk at great length about how GIS intelligence can give businesses the power to expand into areas that were previously thought of as unprofitable due to income and unemployment rates of the area. They give an example of a Chili's that opened and was successful in a neighborhood that other restaurant franchises had written off. Examples such as this brings the concern of how much power GIS can bring and the unintended consequences of such decisions. In this case, the installment of a Chili's could be the first step in the gentrification of a neighborhood. Gentrification is the "act of changing a neighborhood to fit middle class tastes." When gentrification happens, it raises the property values of the area and makes it more expensive to live there. Often times this means that lower income households are driven out because they cannot afford to live there anymore. It may be difficult for a single, profit driven business to think of this at the time of expansion.
When opening the City Data link, the default map that displayed is the median household income by state. My first observation is that whatever software was used to generate the map is very much like the BAO software that we use in class. Secondly, I noticed that states with some of the nation's largest cities (California, New York, Washington, Maryland) had higher median incomes. I was curious to see how the wealth was distributed in theses states so I zoomed in on California to the county level and found that bay area counties (San Francisco County, Marin County, San Mateo County, Santa Clara County) had the highest median income in the state by far; even more than Orange County. It also is interesting to see that bay area counties are smaller than SoCal counties by area. This could contribute to the data looking how it does as smaller bay area counties capture pockets of very high wealth neighborhoods.
The video of 200 countries, 200 years was very very interesting. Katie has said before that data analytics can often times create more questions that it answers. This video is a fine example. I can see that major world events such as the industrial revolution and WWI had major impacts on the movement of the countries. However, what happened to China between 1956 and 1957 that caused a tremendous drop in life expectancy and why did it jump back up again in 1958? What would this look like if it was only US states?
Azaz's article regarding the used of GIS is business was insightful to gain a perspective on a broad range of GIS uses. I found that many of the sources used in the article were very dated: some being from the 2000's but most from the early-mid 90's. One claim they made was that McDonald's was seen as the industry leader among its competitors in terms of using GIS technology. That assertion was made based off a source published in 1995. I wonder if more recent analysis would reflect this.
