file:///Users/rebeccahuteson/Downloads/Synder-2013-NED.pdf

The Benefits of Improve National Elevation Data

Elevation data is useful for many reasons, but most current US elevation data is at least 30 years old. The National Enhanced Elevation Assessment (NEEA) was done in 2011 to assess the need for new elevation data to be collected.

Lidar, light detection and ranging, can be used to survey elevations, which can reduce the time it takes to update maps, detect fault ruptures to avoid catastrophes, makes surveying safer, makes aviation safer, improves the precision of farming, reduces the time needed for flood risks analysis, collect forest information to determine environmental concerns, detect variation in farm fields to ensure farmers apply the right amounts of chemicals and there is less wasted, locate efficient wind farming locations, survey land for oil and gas companies, and can help locate the best routes for roads, which can save gas and make driving safer.

 After reviewing the benefits and costs of collecting elevation data with different accuracy levels and data collection cycles, it was determined that each collection situation, except quality level one collected annually, would result in savings. Different areas require different levels of accuracy’s with different data collection cycles for optimal savings.

Availability of elevation data and technology available for collecting this data are changing. Improvements in laser and satellite technologies are increasing accuracy and density of Lidar surveys.

The NEEA found that upgrading the nations elevation data would benefit all levels of business and government operations, collecting data over larger areas leads to greater savings, and there are no drawbacks to implementing a national program. The USGS developed the 3DEP Initiative in response to the findings. Elevation data will be collected for the US on an eight-year cycle, and IFSAR (Interferometric synthetic aperture radar) will be used to collect data for Alaska. In addition to the already states benefits, 3DEP will create new jobs and transform the geospatial community.

 Citation:

Snyder, Gregory I. (2013). The Benefits of Improve National Elevation Data. Photogrammetric Engineering and Remote Sensing, Retrieved from file:///Users/rebeccahuteson/Downloads/Synder-2013-NED.pdf

GIS Tool, LIDAR, Helps Responders See Flood Levels in New Orleans after Hurricane Katrina

According to Dean Gesch, a government research physical scientist, a new remote-sensing technology called LIDAR, which is an acronym for light detection and ranging, proved itself worthy in 2005 when in New Orleans, hurricane Katrina struck. There was a lack of aerial photo data available to judge the flood levels, so LIDAR, which uses light to show data about elevation, was called upon to display elevation information. This would later come into play when responders had to figure out levels of flooding in different areas of town. This elevation data gathered with LIDAR was paired with measurements taken from a lake-level flood gauge. By the time the water was settled, LIDAR and the information from the flood gauge were combined and able to display an indication of what the flood volume looked like in different areas of New Orleans.

As you can imagine, this is very important information for responders to know so that they can appropriately distribute resources and know which areas are in urgent need of help. This is a real-world scenario about how GIS tools such as LIDAR can be more important in our lives than just being some “complicated technology” that nobody wants to pay attention to or take the time to learn. Also, this gives way to the fact that if more people were working in this industry and keeping data about human environments up to date, we could be better prepared to deal with natural disasters by being able to have more accurate representations of our environment and therefore understanding how to protect ourselves.

Works Cited

Gesch, D. (2005). Topography-based analysis of Hurricane Katrina inundation of New Orleans. Science and the storms: The USGS Response to the Hurricanes of.

Finding Success in a Soft Economy

file:///Users/rebeccahuteson/Downloads/ESRI-improving-retail-performance.pdf

For retail businesses to be successful, it is important for them to understand location information.  Businesses need to understand their markets needs. They need to understand the different dynamics of different locations in order to be successful.

ArcGIS Business Analyst allows retailers to analyze the market of certain locations. Markets are changing and retailers are finding it harder to succeed. The analyst allows the business to see patterns of successful businesses and copy them.

Markets are changing due to the declining economy. Young adults, however, who aren’t effected by real estate, retirement, and investment markets, continue to spend at the same level. ArcGIS Business can analyze sale records and the customer base, giving retailers information about where to locate businesses and what types of merchandise to sell.

United Properties, which owns shopping centers in the Midwest, decided it needed to use GIS data to give its leasers information about the market so they could succeed. They chose Esri ArcGIS server, ArcGIS Mapping for Sharepoint, and the business analyst online API. These tools create interactive maps that report demographic data, helping them find the right location. Users can create reports comparing the retail value of different locations. 

 Nike licensed GIS software to understand where the market for their shoes was. They also use it to find where shoes from their Reuse a Shoe program should be distributed. GIS maps save time because one can be made, and it can be applied to many different retailers.

When the tourist town of Hershey, Pennsylvania began experiencing a downturn, a GIS consulting firm was called. They found that highway systems were directing tourists away from downtown Hershey. They decided they needed to revitalize the downtown area. They used the Huff gravity model in the Business Analyst to figure out if people would be willing to drive far to get downtown. Based on the Huff model, they created a design to fit the market.

Esri Business Analyst Online helped real estate owners figure out what type of restaurant would be most successful in a recently closed barbeque restaurant. The local market was mapped out on the business analyst. The area fit the demographic profile that most Old Spaghetti Factory restaurants typically served.

The success of the shopping centers owned by Evans and Avant can be attributed to the market research they have done. They use Esri Business Analyst Software so their clients choose the best locations for their businesses. Business analyst characterizes neighborhoods so retailers understand their potential markets.

Citation:

Esri. (2012). Improving retail Performance With Location Analytics [Data File]. Retrieved from file:///Users/rebeccahuteson/Downloads/ESRI-improving-retail-performance.pdf

Mexican Americans at increased risk for obesity and diabetes!

In the study Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas, 2004-2007 and driven by Susan P. Fisher-Hoch and her peers, we learn some attributes consistent with the Mexican American community of Cameron County. Indeed, when it comes to obesity and diabetes, this special community on the Mexican American border appears to have differences with Americans. In fact, the research has discovered that Mexican Americans are more likely to be obese or to develop diabetes than others.

The method to carry out this study was based on a cohort on the US-Mexican border in the city of Brownsville, Texas. Susan P. Fisher-Hoch and her research group wanted to discover if any minor socio-economical advantages would affect the risk of obesity and diabetes for the Mexican American population of Brownsville. Thus, on the basis of 2000 census data, they divided the Mexican American population of Brownsville into four strata differing by their annual income. Then started inviting all the households from the selected census blocks to participate in the study. The selected census blocks refer only to the first strata, the “lower income” ($17,830 or less) and the third strata, the “higher income” ($24,067 to $31,747). They finally randomly pick one person from each household in order to participate in this study.

Following the selection process, the participants were asked to take a battery of tests such as blood analysis, blood pressure, blood glucose level, insulin level, height and weight measurements, body mass index (BMI) and waist circumference.

When they had all the data they needed, they ranked the participants by household income and decided to select the top and bottom quartiles in order to obtain a wider difference in household income than the one provided by the use of the full census data. Therefore, the comparison was made between the top 202 participants and the bottom 202 participants on the household income’s basis.

What about GIS?

It gets interesting when they visualized the spatial distribution of households by income with the geographic information system. They collected via Global Positioning System the longitude and latitude coordinates of households and geocoded them on ArcMap 8.3.

The result is relevant. Effectively, it shows a map with two main cluster well defined. We can observe a tendency for the “lower income” to be close from the border whilst the “higher income” is further from the border and more spread out. We can easily imagine the land cost being the cause of this spatial distribution.

Obesity at its best

The results of the research provide important information concerning Mexican Americans health. Firstly and surprisingly, they found no significant difference in the prevalence of obesity and diabetes between the two different socio-economical stratospheres’.  On the other hand, the numbers do indicate a serious health issue that must be addressed.

More than 50% of the participants are obese and 8% of them are morbidly obese.  That is 1.4 times higher than numbers reported nationally for Mexican Americans.

A fourth have diabetes and nearly one in ten participants of the “lower income” were informed through this study that they have diabetes. In fact, 78% of the participants did not have health insurance and, most likely, because of their incomes being too low for them to procure one.

We could question where is the equitable health care system? Should health care be accessible for everyone? Mainly when you know that “In 2006, more than 20 million Americans were estimated to have type 2 diabetes and by 2050, the number of US patients with diagnosed diabetes is projected to rise to 39 million." We need to understand the dramatic issue of this important health matter touching the Mexican American community, especially in Brownsville, Texas.

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Reference: Fisher-Hoch SP, Rentfro AR, Salinas JJ, Pérez A, Brown HS, Reininger BM, et al. Socioeconomic status and prevalence of obesity and diabe- tes in a Mexican American community, Cameron County, Texas, 2004-2007. Prev Chronic Dis 2010;7(3). http://www. cdc.gov/pcd/issues/2010/may/09_0170.htm. Accessed [9/7/2014].

www.cdc.gov/pcd/issues/2010/may/09_0170.htm

How accessible are community and health resources? A study maps distance to health facilities.

Location is a vital factor when looking at people’s accessibility to resources and even healthiness.  There are many ways to examine the relationship between health and location, and distance to health facilities and community resources is one of them.  In addition to the benefits gained from living closer to the facilities, people would also have more free time if they were closer to them.  On the other side of the distance spectrum, there are “food deserts” – areas where grocers are difficult to access.  The point of this article was to showcase the ability of GIS to map neighborhood access to community resources, and the importance this will have to health researchers who can use this data to investigate the effects of distance to community resources and health facilities.

In their study, the setting of the data and maps span all of New Zealand, including rural and urban settings.  The data were gathered from the smallest unit of measurement in the New Zealand census, the meshblock. There were 16 types of facilities within five domains chosen to be categorized as health related. The authors found their data from a variety of governmental resources in New Zealand. The first domain was for recreational amenities, including parks, sports/leisure areas, and beaches.  The second was for shopping facilities, comprised of supermarkets and dairy/fruit/vegetable vendors.  The third was for educational facilities: Daycare/playgrounds, elementary schools, middle/high schools, and colleges.  The fourth was for specific health facilities:  general practitioners, pharmacies, accident and emergency, child care services, ambulances, and fire stations.  The final category was reserved for the Marae, which are facilities meant for the service of Maori people and culture.

Unsurprisingly, the map shows easy access in New Zealand’s big cities.  Coastal areas also seem to have more access to community resources than inland areas.  Using means, the most accessible resource was parks at only 2.83 minutes to get there.  Beaches were the least accessible, at an average of 23.22 minutes to get there.  One of the problems with the study was that public transportation was not included in any way, nor was car ownership.  In conclusion, this type of mapping could be very valuable to further research on location and health, and it should be pursued further.

Citations:

Pearce, J., Witten, K., & Bartie, P. (2006). Neighbourhoods and health: a GIS approach to measuring community resource accessibility. Journal of epidemiology and community health, 60(5), 389-395.  file:///C:/Users/rossd/Downloads/Pearce-2006-NeighborhoodEffects_GIS.pdf

GIS Used to Map Deforestation Risks and Patterns in Belize

Chomitz and Gray looked at Belize and formulated models to try to predict the effects of building roads, logging, mining, and agriculture on future levels of deforestation. The authors used land-use and economic models and GIS technology to determine potential threat of deforestation in different areas from human development. With this knowledge the authors worked to assess the trade-offs between development and environmental degradation of different actions. In this way the authors could determine the most productive and least damaging route of action. The authors hypothesize that impacts on the forest from road development will differ depending on the area and local conditions (such as proximity to towns and soil conditions). In some cases the clearing that takes place to build the roads are not the biggest concern, it is the door it opens for other people to come in and clear forest for subsistent (growing food to feed yourself) or commercial agriculture (Chomitz & Gray, 489).

 Some studies have attempted to map out areas that are at risk for deforestation, but none have taken as many variables and models into consideration. For those reasons this study is unlike others before it. The authors concluded that both commercial and subsistent agriculture are more common closer to the roads and towns and more sparse deeper into the forest. This means that roads will lead to some level of deforestation around them due to the accessibility they provide. They also found that soil quality and properties have a large influence on the probability of that land being used for agriculture. It was originally believed that natives would plant on most any soil without regard to Nitrogen or soil fertility. This study, however, found that to be completely untrue. There is a distinct pattern of native subsistent agriculture in areas where the soil has high levels of Nitrogen, good levels of potassium, and a balanced pH. The authors also found that soil quality was more influential to the placement of subsistent agriculture and less important to commercial agriculture. Ultimately, the author’s concluded that distance from markets (towns/cities), land quality, and tenure have “strong interactive effects on the likelihood and type of cultivation” (Chomitz & Gray, 501).   

Works Cited

Chomitz, K. M., & Gray, D. A. (1996). Roads, land use, and deforestation: a spatial model applied to Belize. The World Bank Economic Review, 10(3), 487-512.

Population Health-Resources Accessibility Study Started by GIS Research

Long distance between people’s living space and a public resource such as a grocery store is likely not something desirable. It is also thought to be a health issue because long distance means it takes longer for people to get what they need in order to function and live. This notion is what caused a group of researchers in New Zealand to organize information about areas in the country with varying distances between citizens’ living spaces and the resources that help them live.

Summary of Article and Research

            This article representing a study describes research done in New Zealand by data-collectors who calculated distances using computer software between living areas and living resources. These resources were markets for food, medical attention, personal development, and recreation, to name a few. The study emphasized that all of these were linked to health and well-being of individuals. A theory that the distance between citizens’ living spaces and resources they need for physical and mental health has a direct link to the health of that community is a main point brought up in this study.

Data that the researchers pulled were sorted into five categories, ranging in distance to these resources. Category 1 included areas of the country that were closest to resources, and category 5 included areas of the country that were the furthest away. The team of researchers calculated the distance between a living space and a resource using GIS (Graphing Information Systems). This is basically software that requires a level of knowledge and practice to use, but once learned can help make accurate maps of various types. The team made sure to calculate the distance from living space to public resource using roads instead of a straight-line distance. This was to provide a more accurate simulation of a citizen’s travel to the resource.

The researchers ended up with a map that identified areas of New Zealand according to how well resourced they were. They concluded that the GIS software which they used to represent their collections of data as a map was helpful, and that they wanted to use their organized research as a base to continue exploration into public health. This highlights the significance of GIS work.

What I Noticed/Questions Raised

As the researchers mentioned, it was noticeable that the data they collected about the country’s citizens and their proximity to these valuable resources came from sources which are objective. I think a next step would to be to incorporate subject-oriented information such as reasons behind trends of resource location, and consumers’ satisfaction level with their resources along with their dissatisfaction level.

Where does mental health and psychology come into play?

Are people who live closer to resources happier because of their quick access to the resources, or possibly more distracted by noise associated with busy places, stressed because of large crowds, and disheartened by a trickling pace of transportation in an area of dense population and traffic? I think mental health is important to a citizenry and think that mental health resources should also be worked with.

            Why are the amount of some resources low and have a high average of traveling distance from living areas?

Looking at the researchers’ tables, I noticed hospitals were often further away than most other resources. Is this because they are more complex facilities to operate, it is a more restricted field of employment, is it a high-stress job, or for all of these reasons?

            Why are the amount of some resources high and have a low average of traveling distance from living areas?

                        It seems like resources such as parks and recreational facilities such as pools are more spread out, based on what I gathered from the data. Is this because they are easier to operate and less complex, more enjoyable places to work at, they are financially easier to sustain, or all of the above?

                       

My Opinion on the Study

            The team seemed to do as accurate of a job as possible considering all the elements of error present when compiling data and making sure it was relevant. I think psychological factors such as media influence, individual experience, and safety should be looked at more closely in research like this. Some places may carry negative associations with some of the population for whatever reason, and therefore they might not want to use the particular resources, making them irrelevant. Through surveying the population, I suggest highlighting resources that are known for things such as a high rating of customer service and general helpfulness. Ideas such as these often play a larger part in the lives of citizens than can be accounted for in data.

Works Cited

I consulted this work to write the above information and give it full-credit towards my knowledge of the subject:

Pearce, J., Witten, K., & Bartie, P. (2006). Neighbourhoods and health: a GIS approach to measuring community resource accessibility. Journal of epidemiology and community health, 60(5), 389-395.

Quantifying the extent and cost of food deserts in Lawrence, Kansas, USA

Quantifying the extent and cost of food deserts in Lawrence, Kansas, USA

            In an article by Lucius F. Hallett IV and Dave McDermott, ‘food deserts’ in the small town of Lawrence, Kansas, are examined and calculated with the use of geographic information systems (GIS) and an array of surveys. The term ‘food desert’ was first used in the early 1990’s to refer to areas where there is no access to adequate, nutritional food sources. In order to compose their map, important factors such as the location of grocery stores, distribution of population, cost of access to food, and information regarding shopping preference had to be quantified.

            When determining the location of grocery stores, Hallett and McDermott decided that they would only include full-service stores, or stores with a size of, or greater than, 30,000 square feet. The sizes of the stores were measured by using air photos from the National Agricultural Imagery Program (NAIP). In addition to the location of the stores, 4,000 surveys were sent out to the four zip codes within the city of Lawrence. These surveys asked shoppers questions regarding forms of transportation taken to the grocery store and, also, travel time and distance. Due to the fact that travel is required to obtain food, a cost surface analysis of total expenditure is created. Hallett and McDermott measured the total cost of travel, the total cost of food, and then compared the two figures to determine which of the survey participants spent the most amount of their time and money to obtain food and in what area of Lawrence they were located.

           The location of the full-service grocery stores can be seen in the map above. There are areas in Lawrence where it appears that the stores are either evenly distributed (Northwest corner), closely grouped (Southeast corner), or almost non-existent (Southwest corner).

            Using GIS to assign values to the routes that can be taken to grocery stores and calculating the cost of transportation along those routes, Hallett and McDermott concluded that no resident in Lawrence that owned a car lived in an underserved area. In contrast, residents without cars that live in older neighborhoods were found to be underserved when their food costs were compared to their food cost, travel time, and distance required to travel in order to get food. In short, those who owned cars were not affected by the food desert phenomenon, while those without cars were affected.

After assigning colors to the different levels of poverty in Lawrence, Kansas, it becomes clear that these supermarket chain stores are more likely to be found in the under-served areas of the city. Conversely, the inner city contains almost no supermarkets. This seems to portray a possible food desert, but due to the lack of poverty in the inner city, shoppers in this area are more likely to shop at smaller, high end stores, which are not represented on this map.

            The overall cost of food is ultimately considered to be spatial. In areas where the population is primarily under-served, big name, chain grocery stores are likely to be found due to their lower cost of food. Conversely, higher priced stores are likely to be found in more affluent neighborhoods due to the preferences of shoppers. Hallett and McDermott conclude that an inherent value can be placed on food, based on perceptions, ideas and lifestyle. The process of obtaining food is relative to each of the different people walking or driving to the store and even by quantifying the time that is spent, distance that is traveled, and the cost of obtaining food, the lines of what is technically considered a food desert in Lawrence, Kansas, are still blurred and open to interpretation.

Hallett, L., & McDermott, D. (2011). Quantifying the extent and cost of food deserts in Lawrence, Kansas, USA. Applied Geography, 31, 1210-1215.

https://lms.southwestern.edu/file.php/5760/Literature/Hallett-2011-Quantiying_food_deserts.pdf

GTography - Williamson County's Food Establishments Classified by Health Inspection Scores

This map displays all of the food establishments classified by their health inspection scores in Williamson County in Texas. Health inspection score data was retrieved from www.wcchd.org. The data was collected approximately in the last 4 years (2010-2014). Food establishments are rated on a scale from 0-45 (0 = fewest health violations, 45 = most health violations). Different types of icons in the restaurants’ location on the map correlate to different classes of scores (see legend). When looking up the restaurant of your choice, check it against this map to make sure you pick a sanitary establishment!

GTography