Geographic Information Systems Temporal GIS Lecture 8 Eng. Osama Dawoud

Spatio-temporal data Time can be considered as fourth dimension. From temporality point of view, there are two types of information namely: Static and dynamic, which must be modeled under a temporal GIS.

Static information Most of phenomena in the real world are dynamic in nature, thus the term static can only be used for objects such as cartographic maps, roads, facilities, utilities, etc, that may not change in a short period of time, however, all of these objects will change during a long period of time.

Dynamic information Dynamic information refers to that information of geospatial objects that change in a short period of time. The length of this period is defined according to its usage fields. ccording to working domain, one can consider the following dynamic aspects of spatial information: Geometrical changes of features over time (such as urban expansion) Positional changes of features over time (such as car movement) Change of features attribute over time (such as traffic volume) Any combination of the above changes

Dynamic information According to the length of duration of time, one can divide dynamic information as follow: Real time data Near real time data Time stamped data

Real Time Data The term real time, about GIS refers to capability of management, visualization and analysis of graphical and attribute information as soon as they input to GIS. The term real time, about data refers to those kinds of data (such as traffic volume) that are collected and imported to GIS as soon as an events occurs.

Near real time data Real time is related to momentary updating, visualization and analysis. Because of possible complex processes and analysis that may be needed for such data before they can be used in GIS, applying such data is so difficult and even in some cases is impossible. Therefore in these cases phrase near real time may be more adoptive.

Time stamped data Time stamped data refers to such data that a time represented with it in some ways (for example as an attribute). There are different components of time that can be considered about an event as follows: When an event occurred in real world (valid time) Occurrence duration of an event When information about an event imported to GIS When those data retrieved and manipulated (transaction time)

Temporal visualization and representation Some approaches that are offered for representation of temporal information in a temporal GIS are as follows: Animation 3D representation Multimedia representation

Temporal visualization and representation Visualization of temporal information in a temporal GIS can be considered from the following aspects: Representation of changes in a database (changes in traffic volume) Representation of changes in a geometrical data (changes in roads network) Representation of changes in a database as well as in graphics (movement of vehicles)

Time stamping spatio-temporal modeling in GIS Some approaches of spatio-temporal modeling in GIS as follows: Snapshot model Space time composite model (STC) The event-based model The space-time cube model

Snapshot model In the snapshot model, when an event occurs, new layer is constructed and occurrence time is stamped to the layer (all of the information, changed or not changed, are stored in the layer). Inconsistency and data redundancy are major disadvantages of this approach. The snapshot model is the most common one in the Earth sciences, as satellite imagery is such an important base data source for them. After image classification of several images of the same area, we essentially have obtained a field-based snapshot sequence that might function as a basis for study with time-related questions

Space time composite model (STC) The space-time composite model also starts from a two- dimensional view of the study space at a given start time. Every change of an object that happens later is projected onto the initial data layer and is intersected with the existing features. This leads to successive intersections, thereby creating an incrementally built, finer polygon mesh. Over time, more and more polygons will be stored in the data layer. Every polygon in this mesh has its attribute history stored with it. This model can be useful if the amount of changes is limited, and changes are discrete steps, as is the case for instance in cadastral applications, where parcels may be split or joined.

The event-based model In an event-based model, we start with an initial state and record events along the time line. Whenever a change occurs, an entry is recorded. This is a timebased model. The spatial and thematic attribute domains are secondary. Our event records on the event-based model are such that we can reconstruct the full spatial and non-spatial history of our study area. This reconstruction will require some or much computation. This, therefore, is a model with low storage consumption but with high costs in computation.

The space-time cube model Like the previous one, this model is based on a two- dimensional view of the study space (spanned by the x- and y-axis), in which geographic phenomena are traced through time (along the t-axis) thereby creating a three-dimensional space-time cube. Multiple, concurrent object changes are even more difficult to guard topologically, and the rules of full topological consistency under continuous change are not even well- understood.

The space-time cube model