Abstract
Spatial data are data that are linked to geographical locations, and hence
can be presented on a map. Most statistical methods for analysing spatial
data operate under the premise that data collected at locations close together
tend to be more alike than data collected at locations further apart.
Much of the methodology developed for analysing spatial data mimics that
of analysing time series data (data correlated over time), where the data
have a natural temporal ordering. However, for spatial data no such ordering
is generally present, and this prevents a straightforward extension
of time series methods to spatial data. Cressie (1993) separated spatial
data into three classes: 1) spatially continuous (geostatistical) data, 2) area
(lattice) data, and 3) spatial point process data. We present the diflFerent
types of spatial data and show how they can be presented on a modem map.
Spatio-temporal data are data that are linked to locations in both space
and time, and hence can be presented as time series for a specific location
in space, on a map for a specific point in time, or EIS a temporaJ sequence of
maps using an animation.
can be presented on a map. Most statistical methods for analysing spatial
data operate under the premise that data collected at locations close together
tend to be more alike than data collected at locations further apart.
Much of the methodology developed for analysing spatial data mimics that
of analysing time series data (data correlated over time), where the data
have a natural temporal ordering. However, for spatial data no such ordering
is generally present, and this prevents a straightforward extension
of time series methods to spatial data. Cressie (1993) separated spatial
data into three classes: 1) spatially continuous (geostatistical) data, 2) area
(lattice) data, and 3) spatial point process data. We present the diflFerent
types of spatial data and show how they can be presented on a modem map.
Spatio-temporal data are data that are linked to locations in both space
and time, and hence can be presented as time series for a specific location
in space, on a map for a specific point in time, or EIS a temporaJ sequence of
maps using an animation.
| Originalsprog | Engelsk |
|---|---|
| Titel | Symposium i anvendt statistisk : 2016 |
| Redaktører | Peter Linde |
| Antal sider | 2 |
| Publikationsdato | 25 jan. 2016 |
| Sider | 43-44 |
| ISBN (Trykt) | 978-87-501-2210-4 |
| Status | Udgivet - 25 jan. 2016 |
| Begivenhed | Symposium i Anvendt Statistik - Copenhagen Business School (CBS), København, Danmark Varighed: 25 maj 2016 → 27 maj 2016 Konferencens nummer: 38. http://www.statistiksymposium.dk/ |
Andet
| Andet | Symposium i Anvendt Statistik |
|---|---|
| Nummer | 38. |
| Lokation | Copenhagen Business School (CBS) |
| Land/Område | Danmark |
| By | København |
| Periode | 25/05/2016 → 27/05/2016 |
| Internetadresse |