Question
As stated by Waldo Tobler, the “first law of geography” finds the relatedness of two places to be proportional to this variable, driving its namesake “decay.” For 10 points each:
[10e] Name this variable whose “friction” underlies the importance of cost path analyses that aim to minimize this variable. For two points on a map, this variable can be estimated using the map’s scale.
ANSWER: distance [accept similar answers like proximity or nearness; accept graph distance; accept distance decay or friction of distance]
[10h] This most common geostatistical interpolation method assumes positive spatial autocorrelation, meaning that closer data points are most similar. ArcGIS models autocorrelation with a semivariogram (“semi-vario-gram”) when using this technique.
ANSWER: kriging (“KREEG-ing”) [or word forms of krige]
[10m] This proponent of New Economic Geography used friction of distance theories in his core–periphery model to explain why industries localize due to transportation costs. He won a Nobel Prize in Economics for that analysis in 2008.
ANSWER: Paul Krugman [or Paul Robin Krugman]
<Other>
Summary
| 2024 ACF Regionals @ Berkeley | 01/27/2024 | Y | 3 | 16.67 | 100% | 33% | 33% |
| 2024 ACF Regionals @ Cornell | 01/27/2024 | Y | 2 | 10.00 | 100% | 0% | 0% |
| 2024 ACF Regionals @ JMU | 01/27/2024 | Y | 7 | 12.86 | 100% | 29% | 0% |
| 2024 ACF Regionals @ Nebraska | 01/27/2024 | Y | 6 | 16.67 | 100% | 67% | 0% |
| 2024 ACF Regionals @ Ohio State | 01/27/2024 | Y | 3 | 10.00 | 100% | 0% | 0% |
| 2024 ACF Regionals @ Rutgers | 01/27/2024 | Y | 5 | 14.00 | 100% | 20% | 20% |
| 2024 ACF Regionals @ Imperial | 01/27/2024 | Y | 7 | 12.86 | 100% | 29% | 0% |
| 2024 ACF Regionals @ Vanderbilt | 01/27/2024 | Y | 5 | 12.00 | 100% | 20% | 0% |
| 2024 ACF Regionals @ MIT | 01/27/2024 | Y | 2 | 15.00 | 100% | 50% | 0% |
Data
| Cornell A | Cornell C | 10 | 0 | 0 | 10 |
| Cornell B | RIT B | 10 | 0 | 0 | 10 |
| Berkeley A | Stanford A | 10 | 0 | 0 | 10 |
| Berkeley B | Berkeley C | 10 | 0 | 0 | 10 |
| Stanford B | Stanford C | 10 | 10 | 10 | 30 |
| Virginia B (UG) | GWU A (UG) | 10 | 0 | 0 | 10 |
| GWU B (Grad) | Liberty B (DII) | 10 | 0 | 0 | 10 |
| Duke A (UG) | JMU B (UG) | 10 | 0 | 0 | 10 |
| UNC B (UG) | Liberty C (DII) | 10 | 0 | 0 | 10 |
| Maryland A (Grad) | Maryland C (DII) | 10 | 0 | 10 | 20 |
| Maryland B (UG) | Virginia C (UG) | 10 | 0 | 0 | 10 |
| UNC A (Grad) | Virginia A (UG) | 10 | 0 | 10 | 20 |
| Claremont A | Iowa B | 10 | 0 | 10 | 20 |
| Iowa A | Appalachian State | 10 | 0 | 10 | 20 |
| UBC A | Rice A | 10 | 0 | 10 | 20 |
| Sorbonne | Michigan State A | 10 | 0 | 10 | 20 |
| Texas A&M B | Texas A&M A | 10 | 0 | 0 | 10 |
| Texas A | UW A | 10 | 0 | 0 | 10 |
| Kenyon | Michigan C | 10 | 0 | 0 | 10 |
| Michigan A | Ohio State B | 10 | 0 | 0 | 10 |
| Michigan B | Ohio State A | 10 | 0 | 0 | 10 |
| John Jay | Penn | 10 | 10 | 10 | 30 |
| NYU B | Haverford | 10 | 0 | 0 | 10 |
| Rowan | NYU A | 10 | 0 | 0 | 10 |
| Columbia C | Vassar | 10 | 0 | 0 | 10 |
| Oxford A | Kiel | 10 | 0 | 10 | 20 |
| Cambridge A | Sheffield | 10 | 0 | 0 | 10 |
| Bristol | Oxford C | 10 | 0 | 0 | 10 |
| Cambridge B | Oxford B | 10 | 0 | 0 | 10 |
| Durham A | Warwick | 10 | 0 | 0 | 10 |
| Edinburgh | Imperial B | 10 | 0 | 10 | 20 |
| Imperial A | Durham B | 10 | 0 | 0 | 10 |
| Johns Hopkins | Columbia B | 10 | 0 | 0 | 10 |
| Alabama | Georgia Tech B | 10 | 0 | 0 | 10 |
| Tennessee | Geodesic | 10 | 0 | 0 | 10 |
| Harding | Kentucky | 10 | 0 | 0 | 10 |
| MTSU | Vanderbilt A | 10 | 0 | 10 | 20 |
| Georgia Tech A | South Carolina | 10 | 0 | 0 | 10 |
| Dartmouth B | Dartmouth A | 10 | 0 | 10 | 20 |
| Harvard B | Harvard A | 10 | 0 | 0 | 10 |