Tutorial 1: Heart Disease Rate \u2013 2016 Southern U.S. Hot Spots by County<\/p>\n
My single hot spot map (HeartDisease_Elizabeth_Lee) <\/a>represents the 2016 heart disease rates (deaths divided by population) of Southern U.S. counties by showing hot spots (and cold spots and non-significant areas).\u00a0 The CDC Wonder Heart Disease data was obtained from http:\/\/wonder.cdc.gov<\/a> and brought into an ArcGIS file geodatabase.\u00a0 I created two models: one to analyze the data into feature classes for each year and one to generate hot spot analyses for each year.\u00a0 These hot spot maps were then animated.<\/p>\n In my 2016 map, cold spots can mainly be seen in Texas, Kentucky, Tennessee, Virginia, Maryland, North Carolina, South Carolina and Georgia.\u00a0 Hot spots can mainly be seen in Oklahoma, Arkansas, Louisiana, Tennessee, Mississippi, Alabama, Florida and West Virginia.\u00a0 In general, hot spots tend to cluster around the central Southern U.S. area.<\/p>\n Animations give viewers a sense of interactivity as images are seen as having movement.\u00a0 By animating a heart disease hot spot map, the audience can see any changes, movements and patterns.\u00a0 This may be particularly useful to professionals in the healthcare industry such as cardiologists or medical\/health geographers.\u00a0 They may be able to infer something about how the environment correlates with heart disease rates.\u00a0 However, further research will have to be done in order to make conclusions regarding causes of heart disease and any patterns noticed.\u00a0 Other potential explanations for deaths by heart disease besides environment may include genetics or lifestyle.<\/p>\n Lab 1: Edmonton, Alberta Land Use Change, 1966 to 1967<\/p>\n Executive Summary<\/p>\n Landscape changes around Edmonton, Alberta were analyzed from 1966 to 1976.\u00a0 FRAGSTATS metrics were evaluated in order to generate a transition matrix to demonstrate land use changes over the time period.\u00a0 Analyses show more urban built-up areas, less cropland, less non-productive woodland and more productive woodland from 1966 to 1976. \u00a0The city of Edmonton should consider a food and land policy taking into account the needs of its citizens, urbanization and future developments.<\/p>\n Introduction<\/p>\n When looking at land use, we must divide the different categories in order to strike a balance between quality of life, social welfare and other issues (Hansen 1984).\u00a0 Urbanized lands comprise of residential, commercial, industrial and other areas (Hansen 1984).\u00a0 As populations grow, people expand into formerly rural areas, deconstructing and reconstructing areas of land use (Hansen 1984).\u00a0 Besides population, factors like age of first marriage, divorce rates, greater lifespan, wealth, and mobility affect households and thus urbanization (Hansen 1984).\u00a0 The province of Alberta has \u201crelatively large urban areas\u201d (Hansen, 1984, p. 68).\u00a0 But if every decade just 1% of agricultural land is lost to urbanization, all of the \u201cgood quality land\u201d in Canada will be gone (Hansen, 1984, p. 79).\u00a0 So, evaluating land use is significant in urban planning and ensuring a brighter future for the city of Edmonton.<\/p>\n Results and Discussion<\/p>\n From GeoGratis, Canada Land Use Monitoring Program (CLUMP) data were downloaded.\u00a0 The vector files were converted to raster files with a 100 meter resolution.\u00a0 After generating class descriptors files for each year, FRAGSTATS analyses were conducted.\u00a0 Class and landscape metrics were evaluated.\u00a0 A transition matrix was generated using a Microsoft Excel PivotTable to observe how land uses in 1966 changed over time.<\/p>\n Analyses show more urban built-up areas, less cropland, less non-productive woodland and more productive woodland from 1966 to 1976 (Fragstats_Map_Elizabeth_Lee<\/a>).\u00a0 99.96% of urban built-up areas in 1966 remain in 1976.\u00a0 However, 21.06% of mines, quarries, sand, and gravel pits; 10.85% of non-productive woodland; and 14.12% of outdoor recreation areas were transformed into urban built-up areas.\u00a0 Only 82.34% of cropland remains cropland from 1966 to 1976.<\/p>\n Only about 8.53% of non-productive woodland remains from 1966 to 1976.\u00a0 Over half (51.62%) is transformed into productive woodland.\u00a0 63.76% of productive woodland in 1966 remains in 1976.\u00a0 But, 51.62% of non-productive woodland and 32.34% of unimproved pasture and rangeland become productive woodland.<\/p>\n Conclusion<\/p>\n The great increases in urban built-up areas a rising issue for Edmonton.\u00a0 This is especially concerning due to decreases in cropland.\u00a0 How will the city cope with the larger populations that likely correlate with urbanization?\u00a0 As more and more people arrive in the city, there are higher demands for housing, services, and of course, food.\u00a0 Many cities in North America see conflicts between residents\u2019 demands for \u201csustainable urban food systems\u201d and municipal development (Beckie, Hanson & Schrader, 2013, p. 16).\u00a0 This is why the city of Edmonton should consider a food and land policy taking into account the needs of its citizens, urbanization and future developments.<\/p>\n Literature Cited<\/p>\n Beckie, M.A., Hanson, L.L. & Schrader, D. 2013. Farms or freeways? Citizen engagement and municipal governance in Edmonton\u2019s food and agriculture strategy development. Journal of Agriculture, Food Systems, and Community Development 4(1), 15\u201331. http:\/\/dx.doi.org\/10. 5304\/jafscd.2013.041.004<\/p>\n \u201cFRAGSTATS METRICS.\u201d Accessed February 1, 2020. http:\/\/www.umass.edu\/landeco\/research\/fragstats\/documents\/Metrics\/Metrics%20TOC.htm<\/a><\/p>\n Hansen, J. A. G. 1984.\u00a0Canadian small settlements and the uptake of agricultural land, 1966\u20131976.\u00a0SOCIAL INDICATORS RESEARCH\u00a015(1): 61-84.<\/p>\n Lab 2: A Child\u2019s Language Skills in Vancouver<\/p>\n The relationship between a child\u2019s language abilities and other variables to the child\u2019s neighbourhood was examined (Elizabeth_Lee_GWR<\/a>).\u00a0 Exploratory regression was used to determine the most important variables to use in the geographically weighted regression (GWR) analysis.\u00a0 With those determined variables, generalized linear regression (GLR) was performed to calculate the statistics correlated with the variables.\u00a0 Then GWR was conducted to examine the spatial relationships with the variables.\u00a0 Box-plots were then generated after using a spatially constrained multivariate clustering analysis tool.\u00a0 It should be noted that the variables explored may not necessarily accurately predict a child\u2019s learning skills.\u00a0 Statistical analyses may not necessarily reflect real-life situations and individual behavior.<\/p>\n Lab 3: Crime Analysis (Jan. ’05 – Mar. ’06, Ottawa-Nepean)<\/p>\n Using CrimeStat, crime data (including residential break and enter crimes, commercial break and enter crime, stolen vehicles and robberies) were examined with nearest neighbour analyses, Knox analysis, Moran’s I statistic, hot spot analysis, nearest neighbour hierarchical spatial clustering and kernel density estimation (both single and dual surfaces).\u00a0 See Crime_Elizabeth_Lee<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" Tutorial 1: Heart Disease Rate \u2013 2016 Southern U.S. Hot Spots by County My single hot spot map (HeartDisease_Elizabeth_Lee) represents the 2016 heart disease rates (deaths divided by population) of Southern U.S. counties by showing hot spots (and cold spots and non-significant areas).\u00a0 The CDC Wonder Heart Disease data was obtained from http:\/\/wonder.cdc.gov and brought […]<\/p>\n","protected":false},"author":29802,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-9","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/9","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/users\/29802"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":4,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/9\/revisions"}],"predecessor-version":[{"id":47,"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/pages\/9\/revisions\/47"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/researchingis\/wp-json\/wp\/v2\/media?parent=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}