{"id":1788,"date":"2016-04-25T13:12:40","date_gmt":"2016-04-25T20:12:40","guid":{"rendered":"http:\/\/blogs.oregonstate.edu\/geo599spatialstatistics\/?p=1788"},"modified":"2016-04-29T13:24:31","modified_gmt":"2016-04-29T20:24:31","slug":"hot-spot_alaska-skate","status":"publish","type":"post","link":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/2016\/04\/25\/hot-spot_alaska-skate\/","title":{"rendered":"Hot Spot Analysis in Arc: Alaska Skate By-Catch Rate in Fisheries Observer Data"},"content":{"rendered":"

\u00a0\u00a0 Question:<\/strong><\/p>\n

Where are the statistically significant regions of high and low Alaska Skate catch rate from commercial longliners in the Bering Sea?<\/p>\n

\"AK_skate_lgl_rate\"<\/a><\/p>\n

Name <\/span><\/strong>of the tool or approach used.<\/span><\/strong><\/p>\n

ArcGIS- Hot Spot Analysis<\/span><\/p>\n

Brief description of steps you followed to complete the analysis.<\/strong><\/p>\n

The dataset that I used to complete this analysis includes over 200 species of fish and four gear types, so I chose to limit the scope of this exercise to one species in one gear type. \u00a0So I narrowed the dataset by selecting Alaska Skates caught by longliners, and ran a Hot Spot Analysis on this selection. \u00a0Within the Hot Spot tool in Arc, I selected catch \u201cRATE\u201d as the input field for comparison, retained the default selections for the spatial relationship (\u201cFIXED_DISTANCE_BAND) and distance method (\u201cEUCLIDEAN_DISTANCE\u201d), and ran the analysis. \u00a0<\/span>
\nNarrowing the temporal scope of the analysis, I ran it again on two subsets of the data. \u00a0One on points from 1998-2007 and the second on points from 2008-2014.<\/span><\/p>\n

Brief description of results obtained.<\/span><\/strong><\/p>\n

\n \"AK_skate-lgl_hotspot\"<\/a>\n
Alaska Skate catch rate hot spot analysis 1998-2014<\/figcaption>\n <\/figure>\n

The original analysis includes AK skate catch rates on Bering Sea longliners from 1998-2014 and reveals some interesting patterns about fishing behavior as well as where skate catch rates are higher. \u00a0There is a clear line of cold spots along the southern edge of the oceanic shelf that runs north of the Aleutian Islands. \u00a0A few distinct hotspots are also revealed in the western Aleutians near Adak and Atka islands<\/span><\/p>\n

\n \"AK_skate-lgl_hotspot98-07\"<\/a>\n
Hot spot analysis on Skate catch rate from 1998-2007.<\/figcaption>\n <\/figure>\n
\n \"AK_skate-lgl_hotspo08-14t\"<\/a>\n
Hot spot analysis of Skate catch rate 2008-2014<\/figcaption>\n <\/figure>\n

Changing the temporal scope of the analysis revealed some very different patterns than the full time period. \u00a0There are a number of hypotheses that can be developed for further exploration as to why the patterns change. \u00a0<\/span><\/p>\n

Critique of the method – what was useful, what was not?<\/strong><\/p>\n

For this dataset, I think that this tool is a quick and easy way to identify patterns at different scales and could be an approach to hypothesis generation. \u00a0This dataset is one that the sample points are independent of the researcher and span a large temporal and spatial range. \u00a0It seems that for this tool these characteristics in the dataset make it more applicable. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\u00a0\u00a0 Question: Where are the statistically significant regions of high and low Alaska Skate catch rate from commercial longliners in the Bering Sea? Name of the tool or approach used. ArcGIS- Hot Spot Analysis Brief description of steps you followed to complete the analysis. The dataset that I used to complete this analysis includes over… Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":7725,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[661527,1],"tags":[],"class_list":["post-1788","post","type-post","status-publish","format-standard","hentry","category-tutorials-2016","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/posts\/1788","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/users\/7725"}],"replies":[{"embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/comments?post=1788"}],"version-history":[{"count":5,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/posts\/1788\/revisions"}],"predecessor-version":[{"id":1808,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/posts\/1788\/revisions\/1808"}],"wp:attachment":[{"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/media?parent=1788"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/categories?post=1788"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/dev.blogs.oregonstate.edu\/geo599spatialstatistics\/wp-json\/wp\/v2\/tags?post=1788"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}