Predictive limitations of spatial interaction models : a non-Gaussian analysis
We present a method to compare spatial interaction models against data based on well known statistical measures that are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the US Census 2000. We find that the radiation model performs significantly worse than an appropriately chosen simple gravity model. Various conclusions are made regarding the development and use of spatial interaction models, including: that spatial interaction models fit badly to data in an absolute sense, that therefore the risk of over-fitting is small and adding additional fitted parameters improves the predictive power of models, and that appropriate choices of input data can improve model fit.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2020 |
|---|---|
| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
Scientific reports - 10(2020), 1 vom: 15. Okt., Seite 17474 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Hilton, B [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 12.11.2020 Date Revised 12.11.2020 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1038/s41598-020-74601-z |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM316303046 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM316303046 | ||
| 003 | DE-627 | ||
| 005 | 20231226202043.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2020 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41598-020-74601-z |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1054.xml |
| 035 | |a (DE-627)NLM316303046 | ||
| 035 | |a (NLM)33060807 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Hilton, B |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predictive limitations of spatial interaction models |b a non-Gaussian analysis |
| 264 | 1 | |c 2020 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 12.11.2020 | ||
| 500 | |a Date Revised 12.11.2020 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a We present a method to compare spatial interaction models against data based on well known statistical measures that are appropriate for such models and data. We illustrate our approach using a widely used example: commuting data, specifically from the US Census 2000. We find that the radiation model performs significantly worse than an appropriately chosen simple gravity model. Various conclusions are made regarding the development and use of spatial interaction models, including: that spatial interaction models fit badly to data in an absolute sense, that therefore the risk of over-fitting is small and adding additional fitted parameters improves the predictive power of models, and that appropriate choices of input data can improve model fit | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Sood, A P |e verfasserin |4 aut | |
| 700 | 1 | |a Evans, T S |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Scientific reports |d 2011 |g 10(2020), 1 vom: 15. Okt., Seite 17474 |w (DE-627)NLM215703936 |x 2045-2322 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2020 |g number:1 |g day:15 |g month:10 |g pages:17474 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41598-020-74601-z |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2020 |e 1 |b 15 |c 10 |h 17474 | ||