{"id":7411,"date":"2026-09-14T16:10:40","date_gmt":"2026-09-14T21:10:40","guid":{"rendered":"https:\/\/sites.owu.edu\/geog-291\/?p=7411"},"modified":"2026-09-14T16:10:40","modified_gmt":"2026-09-14T21:10:40","slug":"jan-week-3","status":"publish","type":"post","link":"https:\/\/sites.owu.edu\/geog-291\/2026\/09\/14\/jan-week-3\/","title":{"rendered":"Jan Week 3"},"content":{"rendered":"

Mapping Density<\/em><\/span><\/h2>\n
    \n
  • Density mapping shows where things cluster instead of where each single feature sits hence it is good for patterns, bad for pinpointing.<\/span><\/em><\/li>\n
  • Matters most when your polygons are different sizes; a raw count map makes a big polygon look busy just because it is big.<\/span><\/em><\/li>\n
  • Two ways you can go about it:<\/b>\n

    <\/b><\/em><\/p>\n

      \n
    1. Area method:<\/b> We divide features by polygon area, or use a dot map. The dots are placed randomly, so they are a picture of density, not real locations. That feels like a trap for anyone who does not read the fine print.<\/span>\n

      <\/span><\/em><\/li>\n

    2. Density method: <\/b>A\u00a0raster where every cell gets a value from the features inside a search radius.<\/span>\n

      <\/span><\/em><\/li>\n<\/ol>\n<\/li>\n

    3. Small cells give a smoother surface but cost processing time. A bigger radius smears the pattern out.\u00a0<\/span><\/em><\/li>\n
    4. The simple method just counts what is in the radius; the weighted method leans toward features near the cell centre and gives a cleaner map.<\/span><\/em><\/li>\n
    5. Display with graduated colours or contour lines.<\/span><\/em><\/li>\n<\/ul>\n

      So we have these classificational schema; Natural Breaks, Quantile, Equal Interval, Standard Deviation which basically decides what the map tells, which is a lot of power for one dropdown. I keep wondering how often public data gets quietly skewed by someone picking an arbitrary search radius. Still, density fixing the unequalnpolygon problem is the real win here.<\/span><\/em><\/p>\n

      Finding What’s Inside<\/span><\/em><\/h2>\n
        \n
      • Mainly for monitoring or comparing; some examples include; drug arrests near a school, or which zip code has more of something.<\/span><\/em><\/li>\n
      • Three methods:<\/b><\/em>\n
          \n
        1. Area > Features: <\/b>More visual. Fast glance, No data out of it.<\/span><\/em><\/li>\n
        2. Select Features: <\/b>\u00a0Gives us a subset we can actually use for lists and summary stats (count, frequency, sum, average).\u00a0<\/span><\/em><\/li>\n
        3. Overlays: <\/b>Merges boundaries and features into a new layer and permanently tags features with the area’s attributes. Vector is precise but leaves slivers; raster counts cells, faster but cell size drives everything.<\/span>\n

          <\/span><\/em><\/li>\n<\/ol>\n<\/li>\n<\/ul>\n

          To be honest Vector still wins for anything legal like parcel boundaries, because “close enough” does not hold up in a property dispute. However, for non high stakes thing the latter should be good enough.<\/span><\/em><\/p>\n

          Finding What’s Nearby<\/span><\/strong><\/em><\/p>\n

            \n
          • In this chapter you ask some other different questions such as who is affected by an event, who is actually served by a facility and so on.\u00a0<\/span><\/em><\/li>\n
          • “Near” is not only physical distance. It can be time, money, or effort, which changes the decision process.<\/span><\/em><\/li>\n
          • Three methods:<\/b><\/em>\n
              \n
            1. Straight line: <\/b>Buffers, Select within distance, or a continuous distance surface. Planar for a flat plane, geodesic for a curved earth. Ranges can be inclusive rings (0\u20131, 0\u20132, 0\u20133 mi) or distinct bands (0\u20131, 1\u20132, 2\u20133 mi).<\/span><\/em><\/li>\n
            2. Cost over a network<\/b>: Streets and other fixed infrastructure.\u00a0<\/span><\/em><\/li>\n
            3. Cost over a surface<\/b>: Overland travel with no roads.\u00a0<\/span><\/em><\/li>\n<\/ol>\n<\/li>\n<\/ul>\n

              The\u00a0 RINGS vs BANDS distinction is the most useful thing in this chapter for me. Bands isolate each ring, so if I ever compare demographics by distance from a facility, that is the one I want. Building turntables from scratch sounds genuinely tedious, but for emergency routing I do not see how you skip it.<\/span><\/em><\/p>\n

               <\/p>\n","protected":false},"excerpt":{"rendered":"

              Mapping Density Density mapping shows where things cluster instead of where each single feature sits hence it is good for patterns, bad for pinpointing. Matters most when your polygons are different sizes; a raw count map makes a big polygon look busy just because it is big. Two ways you can go about it: Area method: We divide features by polygon area, or use a dot map. The dots are placed randomly, so they are a picture of density, not real locations. That feels like a trap for anyone who does not read the fine print. Density method: A\u00a0raster where every cell gets a value from the features inside a search radius. Small cells give a smoother surface but cost processing time. A bigger radius smears the pattern out.\u00a0 The simple method just counts what is in the radius; the weighted method leans toward features near the cell centre and gives a cleaner map. Display with graduated colours or contour lines. So we have these classificational schema; Natural Breaks, Quantile, Equal Interval, Standard Deviation which basically decides what the map tells, which is a lot of power for one dropdown. I keep wondering how often public data gets quietly skewed by someone picking an arbitrary search radius. Still, density fixing the unequalnpolygon problem is the real win here. Finding What’s Inside Mainly for monitoring or comparing; some examples include; drug arrests near a school, or which zip code has more of something. Three methods: Area > Features: More visual. Fast glance, No data out of it. Select Features: \u00a0Gives us a subset we can actually use for lists and summary stats (count, frequency, sum, average).\u00a0 Overlays: Merges boundaries and features into a new layer and permanently tags features with the area’s attributes. Vector is precise but leaves slivers; raster counts cells, faster but cell size drives everything. To be honest Vector still wins for anything legal like parcel boundaries, because “close enough” does not hold up in a property dispute. However, for non high stakes thing the latter should be good enough. Finding What’s Nearby In this chapter you ask some other different questions such as who is affected by an event, who is actually served by a facility and so on.\u00a0 “Near” is not only physical distance. It can be time, money, or effort, which changes the decision process. Three methods: Straight line: Buffers, Select within distance, or a continuous distance surface. Planar for a flat plane, geodesic for a curved earth. Ranges can be inclusive rings (0\u20131, 0\u20132, 0\u20133 mi) or distinct bands (0\u20131, 1\u20132, 2\u20133 mi). Cost over a network: Streets and other fixed infrastructure.\u00a0 Cost over a surface: Overland travel with no roads.\u00a0 The\u00a0 RINGS vs BANDS distinction is the most useful thing in this chapter for me. Bands isolate each ring, so if I ever compare demographics by distance from a facility, that is the one I want. Building turntables from scratch sounds genuinely tedious, but for emergency routing I do not see how you skip it.  <\/p>\n","protected":false},"author":2362,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-7411","post","type-post","status-publish","format-standard","hentry","category-course-student-work"],"_links":{"self":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7411","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/users\/2362"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/comments?post=7411"}],"version-history":[{"count":1,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7411\/revisions"}],"predecessor-version":[{"id":7412,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/posts\/7411\/revisions\/7412"}],"wp:attachment":[{"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/media?parent=7411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/categories?post=7411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.owu.edu\/geog-291\/wp-json\/wp\/v2\/tags?post=7411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}