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Building Business Continuity Plans
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The Mean Center tool, one of the spatial statistics tools in ArcGIS, enhances this type of analysis. The mean center is the average x-coordinate and y-coordinate of all the features in the study area. It is useful for tracking changes in the distribution or comparing distributions among different types of features. The Mean Center tool creates a new point feature class in which each feature represents a mean center. In this scenario, the analyst identifies key personnel and assigns a weight value to each based on how critical that person is to operations. Using the Mean Center tool, analysts can find the optimal site for relocating operations based on how convenient it is for the most critical employees.

Figure 3: Potential evacuation meeting points within one-quarter mile of the office.

Of course, many other factors would be considered for such an analysis. The new geoprocessing framework in ArcGIS allows analysts to string functions together in a visual modeling environment called ModelBuilder. ModelBuilder simplifies the process of creating complex models that can be used to answer key questions. In this example, the model shown in Figure 1 takes into account not only proximity to key employees but also proximity to major roads and existing customers. These factors were evaluated and compared to a layer containing available listings. The result was a list of potential temporary locations that met the criteria.

The model does several interesting things. Not only does it use the Mean Center tool to find the weighted mean center for critical employees, it also creates a density surface of current customers and a surface representing distance to major roads. These layers are combined using the ArcGIS Spatial Analyst extension. In this model, proximity to critical employees is given more importance than other inputs. The model generates a map and a report that lists potential properties and contains links to images of those buildings.

Handling Evacuations

Another example that uses spatial statistics to develop a portion of a BCP identifies evacuation meeting points for employees who work in a specific building. Although businesses typically prefer employees to stay inside a building during an emergency until the situation is fully understood, an evacuation plan should be in place.

The location of the office affects the complexity of this task. It can be simple if the business is located in an office park. However, if the office is located in a densely populated city, such as New York, logistics are more complex. If the emergency is isolated to a single building, it is easier to manage than if multiple, adjacent buildings are affected. In the latter case, the plan must take into account the neighborhood surrounding the affected buildings.

After evacuating the office, employees usually meet at a specific location and decide what needs to be done next. Where should employees regroup? Factors such as how far from the building employees can travel with relative ease (recognizing that some people may have disabilities) and the presence of open spaces that can be used must be considered. These questions are geographic in nature.

The Hot Spot Analysis tool is used to find the location of spatial clusters of high and low attribute values. This tool shows areas where higher than average values tend to be found near each other and where lower than average values tend to be found near each other.

Figure 2 shows a simple model illustrating how the ArcGIS Hot/Cold Spot Analysis tool was used for this analysis. This model geocodes the address of the building in question, then buffers that location by one-quarter mile (assuming all employees can travel that far). It clips a business location dataset that contains the area's daytime population by business address. Using this dataset, the model runs the Hot/Cold Spot Analysis tool to find statistically significant hot and cold spots for building employees.

Figure 4: Potential evacuation meeting points within one-quarter mile of the office were used to generate this surface.

The dark blue points shown in Figure 3 represent statistically significant cold spots—areas with few employees that may serve as good meeting places in the event of an emergency. To more clearly see the areas represented by these points, a continuous surface was created using the ArcGIS Spatial Analyst extension (as shown in Figure 4). The surface reveals more generalized areas representing cold spots in the data, shown as dark blue spots. These areas are potential sites for meeting points. However, more information, perhaps obtained from orthophotos of the area, would be required before selecting a final location.

Conclusion

Commercial organizations using GIS can prepare more robust BCPs that integrate many types of data from multiple sources, incorporate geography, and use new tools developed specifically for spatial statistical analysis. GIS enables BCP analysts to ask different types of questions and obtain better answers than they could previously using aspatial techniques that relied solely on databases, spreadsheets, and traditional business intelligence packages.