Stone, M. (2011). Enhancing the delivery of supplemental nutrition assistance program education through geographic information systems. Journal of Nutrition Education and Behavior, 43(4, Suppl 2), S148-S151. doi: 10.1016/j.jneb.2011.01.009
The California Department of Public Health and the USDA has began an initiative known as The Network for a Healthy California (known as Network) to educate individuals who are eligible for the Supplemental Nutrition Assistance Program (SNAP). In order to be eligible, the person's household income should be below 185% of the federal poverty level.
The Network implements GIS technology to geographically identify areas of California that contain large numbers of eligible individuals. GIS is used to identify eligible census tracts, low-resource schools, and community sites (i.e. unemployment housing or food banks).
Network users also use GIS to create layers of varying information, from the California Fitness-gram scores to the number of fast-food restaurants available to students within a half-mile of school. Currently, the Network has 122 layers available for spatial analysis.
The maps that result from these spatial analyses are used to influence policy makers decisions and engage the neighborhoods that are identified.
Monday, September 30, 2013
Sunday, September 29, 2013
Fisher-Hoch, Susan et al (2010). Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas 2004-2007, Preventing Chronic Disease: Public Health Research, Practice and Policy. 7 (3), 1-10.
This study relied on data from the Cameron County Hispanic Cohort (CCHC) to determine the prevalence of obesity and diabetes in Mexican American populations. Specifically, the study examined the affect of socio-economic status on obseity and diabetes rates. The CCHC now numbers 2,000 people aged 35-60 years. Of these, 810 people were randomly selected for the study.
Income was divided into four stratas. Researchers targeted the first strata (the lowest earning) and the third strata (a higher earning group). The lower earning strata had a median income of $17,830 or less; higher earning, $24,067-$31,747. There were more participants representing the lower strata than the higher.
Two nurses and four field workers, all bilingual and bicultural, conducted the study. They had each participant fast for ten hours prior to examinations and rescheduled the exam if they did not fast. During the exam, they measured weight, height, body mass index (BMI), blood pressure, waist circumfrance, insulin levels and blood glucose levels. BMI was used as a measure of obesity.
Results showed that over half of the participants in both income stratas reported a BMI in the obese range. 57% in the lower income strata were obese; 55.5%, in the higher. Less than a half of participants reported insurance, and only five percent reported Medicaid coverage. More women than men were able to participate in the study, because the men tended to work hourly wage jobs.
The study concludes that income is a direct influencing factor contributing to obesity and diabetes. This study is the first of its kind to focus exclusiviely on Mexican Americans in a border city. Results coincide with already published studies which show that the rate of diabetes in Latino communities is twice that of non-Hispanic white communities.
Saturday, September 28, 2013
Wong, Q. 2001. A remote sensing–GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10): 1999-2014.
The Zhujiang Delta region of China is one of the largest areas of economic concentration in Southern China, and as such has experienced rapid urbanization during the years since the implementation of China’s economic reform policies. It is also one of the richest agricultural regions. With the rapid urbanization process changing the land-use patterns of the region, and keeping in mind the Urban Heat Island phenomenon, in which urban areas tend ot have higher surface temperatures than surrounding areas, Wong attempted to use satellite imaging and GIS analysis technology to study the pattern of urbanization and determine its effects on the surrounding environment.
Wong identified 7 types of land use using LANDSAT images of the region for both 1989 and 1997, and then quantified the changes land use between the two with a matrix. These images were then overlaid in a GIS system to represent the data on land use changes in a single image detailing the overall changes. Another layer, containing data on roads in the region, was added to the GIS system and a buffer was added to that data to determine the correlation between urbanization and the distance from major roadways.
After that, radiant surface temperature data from each year, obtained from LANDSAT TM thermal infrared data, was added to the GIS system and an algorithm was used to determine the change between the 8 year period. Combining these elements in the GIS system allowed for the spatial analysis of the correlation between urbanization trends and increases in surface temperatures between 1989 and 1997.
This allowed Wong to conclude that not only has increased surface temperature correlated to increased urbanization, but also that greater urbanization has occurred in rural areas and in closer proximity to roadways. It also presented interesting data regarding two other land uses. Surface temperature increases were also correlated with barren land use as well as non-traditional horticultural lands, though to a lesser extent. Higher temperatures were also found in certain water bodies, which Wong hypothesized might be a result of increased sediments and other particles in the water from greater urbanization and industrial run-off. This demonstrates other potential uses of GIS analysis in determining impacts of urbanization and land use change on the environment.
Posted by Unknown at 8:54 PM
F. Goodchild, Michael (2011). Scale in GIS: An overview. 130 (2011), 5-9.
This review article discusses the relevance of scale, as a disciplinary problem, within GIS and geomorphology. Three general problems of scale are discussed, then two methods for conceptualizing phenomena, raster vs. vector data, problems specific to GIS & modeling, & lastly, methods of formalizing scale within various disciplines.
The first problem of scale is semantic. Scale is used in 3 senses in science:
1) Cartographers refer to the representative fraction as the parameter that defines the scaling of the earth to a sheet of paper
a. ratio between map/ground
b. defines level of detail, positional accuracy
c. this is undefined for digital data
2) Extent of study area
The second problem (resolution is finite):
1) The earth’s surface is infinitely complex & could be mapped to molecules/smaller
2 2) Praxis determines the most largest (usually most relevant) features
a. This makes me wonder why the largest features are usually most relevant
The third problem (resolution in processes):
a) If the process is significantly influenced by detail smaller than the spatial resolution of the data, then the results of analysis/modeling will be misleading (this is the problem of downscaling which geostatistics seeks to alleviate through the use of an inferred correlogram)
a. This leads me to wonder if there are any processes that require all sorts of different levels of scale
b) Most theories are scale-free
a. can’t tell whether the model’s error (all models are imperfect) is due to spatial-resolution effects or the model, or both
There are two methods for conceptualizing geomorphological phenomena, which are scale-independent theoretical frames:
1) Discrete objects
a. the world’s surface is empty like a table top (it is empty in the sense of it being simply flat, not empty empty or without a coordinate system) except discrete things
b. things can overlap
c. good for biological organisms, vehicles, buildings
d. represented as points, lines, areas, volumes
e. so these would be better able to map interactions between things (even if those things are not things per se), where in the continuous-field this would be lost/aggregated/generalized, esp. raster models?
f. maybe we can make a partial analogy between this and substance ontology?
a. phenomena expressed as mappings from location to value/class, so every location in space-time has exactly one value of each variable
b. topography, soil type, air temperature, land cover class, soil moisture
c. process ontology?
Both of these can be raster or vector data, but raster can’t be laid on curved surfaces
1) Raster data has resolution explicit in the size of its cells
a. smaller cells, more of them = better resolution
b. the intervals between samples defines this
c. in 3-d sampling the vertical dimension is often sampled differently than the horizontals leading to a differential in resolution
i. e.g. remote-viewed 2-d map + field photos of vertical dimension
2) Vector resolution is poorly defined and difficult/impossible to infer a posteriori from the contents of its data sets (as most GIS work is done on already present data sets)
a. if the data’s taken at irregular points the distance between points may be used as resolution – how often is the data taken irregularly?
i. use the minimum, mean, max nearest-neighbor distance?
b. when data’s captured as attributes of areas, it’s represented as a polygon/polyhedra
i. resolution is the infinitely thin boundaries, density of sampling of the boundary, within-area/volume variation that’s replaced by homogeneity, size of areas/volumes
ii. this creates the impression that vector data has infinite resolution
1. at finer resolutions the numbers of areas/volumes increase and their boundaries are given more detail but they’re more homogeneous
GIS encounters a problem that applies to many types of geographic data
-measuring the length of a digitized line
-a vector polyline’s length is easily computed as the sum of straight-line segments
-if we assume each sampled point lies exactly on the true line, we will get an underestimate of the truth because no line has the points lie exactly straight
-the underestimate is by an amount that depends on sampling density
-this applies to all natural features, slope, land cover
-this reminds me of stats (line regression) and calc (instantaneous velocity)
-the issue of zooming out and losing detail/generalizing reminds me of “essentialism” from philosophy, & i’m sure the problem of definition comes up elsewhere in GIS (what variables to use, what sampling method)
-the modified area unit problem shows us that aggregation or intersections of mapping elements (data collection and state lines, for example) will change correlations in a manner that is not random variation from alternative samples
-mandelbrot tells us that the rate of info loss/gain is constant/predictable through scaling laws and exhibits power-law behavior and self-similarity
an image displaying self-similarity:
-geostatistics gives us spatial interpolation to refine the spatial resolution of a point data set artificially through methods such as spatial autocorrelation, correlograms, and variograms
when creating an inferred correlogram does one infer up in levels/”steps” till the desired resolution of the study?
-wavelet analysis (a subset of Fourier analysis) allows the decomposed field variables to vary spatially in a heirarchical fashion – this sounds very interesting
In light of all of this, I am currently wondering about the study of part-whole interactions in relation to resolution and vector/raster datasets.
Posted by Unknown at 4:37 AM