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S patial Data Integration TSTool Documentation S patial Data Integration - 2 5. Symbology – the symbol in formation for each layer (e.g., point symbol, line width, polygon fill color), labels, and other visualization information. The features and attributes are the primary data, and the other information facilitates using the features and attributes. Conside r a data collection station, represented by a point on the map. This station may be located near a river and collect streamflow stage (water depth). The station software may convert the stage to flow or may allow this to be done by other software. The s tation also may collect “climate” (meteorological) data such as precipitation, temperature, wind speed and direction, etc. Each measurement type requires a sensor and the cost of hardware typically controls the number and sensitivity of measuring devices. For data management, the station is typically assigned an alphanumeric station identifier and each data type that is collected is assigned an alphanumeric data type. The data are saved locally as date/value information and are then transmitted to or re quested from a data collecting system. The date/value information is essentially time series. Data units and handling of missing data are considered during implementation of data collection systems. Measurements may be taken regularly (e.g., once every fifteen minutes) or may occur at irregular intervals, perhaps in response to some change in conditions. In nomenclature used with TSTool, the former are called regular time series and the latter irregular, reflected by the data interval (time step). For the discussion purposes, consider only a meteorological station that measures precipitation and temperature. For mapping purposes, a choice may be made to focus on the physical nature of the map, in which case a single “Met Stations” (or “Climate Station s”) layer may be shown, using a single symbol. This is suitable if the measurement types for such a station are consistent throughout a system or only one data type is of interest. It is frequently the case that the real -time data that are collected are managed in a database, with data being archived over time, for example resulting in the following time series for precipitation data: 1. Real -time data (often provisional data available for a short period). 2. Real -time extended data – real -time data collected for the past year, for example, having received limited or no quality control 3. Real -time archived data – real -time data for the historical period, quality controlled 4. Hourly accumulation – for example, convert real -time precipitation data to hourly totals 5. Da ily accumulation – for example, convert hourly precipitation data to daily totals 6. Monthly accumulation – etc. 7. Yearly accumulation – etc. The first two examples are often referred to as “real -time” data whereas the last five examples are often referred to as “historical” data. In a system that is homogeneous, a map layer that shows “Precipitation Stations” will imply that all of the above time series are available at the station. However, in a system where, for various reasons, not all stations have real -time and historical data, more attention to detail may be needed on maps. To address this case, the map could show separate layers for real -time and historical stations (two layers). However, this does not address the issue that there may be multiple cat egories of real -time data and multiple categories of historical data. To address this issue, additional layers might be added for each time series type, using the same or similar symbols for each layer. The limitation in this approach is that the map now has many layers and many of the points will be the same and will therefore symbols will plot on top of each other. 100