Only after the laborious data selection, entry, and georeferencing and
temporal-referencing procedures have been completed can the true potentials of the HGIS be realized. As a digital tool, HGIS offers many advances from manual analysis or that performed in traditional databases (Goodchild, Haining, and Wise 1992). It has the ability to perform on-the-fly analysis, offering new ways to quickly explore the spatial data. The numerous analytical capabilities, from simply
visualizing the data and creating maps to spatial and attribute queries to advanced spatial statistical techniques, permit a thorough documentation of the evolving retail landscape.
Perhaps the most basic analytical tool is the visualization of the data. Displaying the data in an effective manner, however, is also perhaps the most powerful tool in understanding processes and to convey information to an audience. GIS allows the data to be viewed in an infinite number of ways. The data can be symbolized with colours and graphics, representing qualities of the variable in question. The GIS also handles various resolutions well, instantly displaying data from the micro through the macro scales. When the entire urban area is viewed, if an area of interest is found, for example a cluster of dry goods stores along Dundas Street, the software can be used to instantly zoom into this area to examine it in greater detail.
Animation is also available, displaying the dynamic nature of the data. The locations of retailers over time were animated, showing the spread from the core to the periphery between 1844 and 2004. Animation is, however, not possible on the static pages of this thesis. Other media, such as presentations, web-sties and digital books can handle animation. To accommodate the limitations of a thesis presented
in monograph form, each cell of the animation can be recorded as a unique map, grouped together into multipart figures (for example see Figure 4.6).
Among the most valuable visual analyses of the historical data in GIS is the layering of different eras. This involves making an upper layer transparent to view the data in the layer below it (See Gilliland and Novak 2006). In this way the spatial extent is kept constant revealing how the area has changed over time. This method is especially useful for the evolution of the physical landscape. The
buildings in a particular year can be superimposed on those of another. The result shows how the building fabric has changed, revealing details which might not be apparent when compared side to side. The differences can then be studied using the morphological analysis with the high-level of accuracy that GIS affords. The caveat in this case is that the two data layers must be accurately referenced to a common grid so that the super-position lines-up. This method is done to trace the evolution of retail buildings, as well as the lot fabric downtown.
A GIS is a spatially-referenced database, thus permitting the traditional record querying techniques, as well the addition of spatial queries. Typically, the basic queries were conducted in Microsoft Access due to its strengths as a sole database management program, whereas ArcMap was used for the spatial queries1. Queries answered simple questions such as how many retailers were present in a given year, to more complex ones such as the functional breakdown of the retailers by type over a fifty year period.
Spatial queries are utilized to understand spatial relationships and patterns. They can be used to select features based upon their proximity to another feature. They can also group features into specified areas. Queries were used to determine the number of retailers which faced the major roads in the city. They also were used to count the number of retailers within each district of the city.
Although relatively simple, the measurement of distances between two features offers some valuable information which would be unavailable outside of the
1 The various datasets can be housed in a Geodatabase in recent versions of ArcGIS, offering
many of the advantages of standard database management systems; however, this data structure is not implemented for the GIS files pertaining to London. Although there are some problems in dealing with the back and forth between Access and ArcGIS, the added complexity of the Geodatabase structure was not warranted for this project.
GIS. The distances between each retailer and the peak value intersection was calculated, the value stored as a separate attribute field. This figure allows the number of retailers within concentric rings to be discerned. Further, these
measurements are compared with other attributes, such as the retail types, and land values.
More advanced spatial statistical techniques are possible with the tools available in the ArcMap software. Cluster analysis was performed in the dry goods and butchers in the nineteenth and early-twentieth centuries. Specifically, standard deviational ellipses were created for each retail type in each era. The sizes of these ellipses quantify the degree of clustering while their orientation indicates directional patterns in the data.
Morphological analysis is also performed using the GIS. Areas of lots, blocks and buildings are calculated for the digitized representations of these features. This most basic morphological measure is time-consuming to produce in the pre-digital era; however, it is instantaneously calculated in GIS once the laborious task of digitizing the features is complete. Comparing the ratio of building areas to lot and block areas produces density measures. Lengths, widths, and perimeters of
features can be calculated with similar ease to describe their shape.
Now that the data sources used have been considered and the processes of creating and using a Historical Geographic Information System have been detailed, it is time to show how the system has been used. Using these expansive datasets and the power of GIS for their analysis, the evolution of the retail landscape is examined. Moving from the macro the micro scale, the GIS is then tasked with studying the central retail district at its apex at the turn of the twentieth century. In the final substantive results chapter, the morphology of planned shopping centres is linked with their success. Notes pertaining to considerations and idiosyncrasies of the data sources and their analyses are included as footnotes in these forthcoming chapters.