Information and Knowledge Management using ArcGIS ModelBuilder

Vered Holzmann and Ben Holzmann
solicited through various channels of communication stressing subjects of common interests, thus,
encouraging the members of these homogeneous groups to effectively contribute to the campaign.
A single volunteer is normally nominated to represent all the group members and function as their
speaker, and taking the responsibility to moderate the group’s performance while providing the
campaign management with reliable classifications pertaining to the member’s positions and opinions
regarding the candidate’s behavior.
Every methodology used to identify these different groups of prospective supporters hinges on the
ability of the campaign’s analysts to utilize the continuously accumulated knowledge in order to
update the specifications defining the criteria for the clustering processes. Cluster analysis was
defined by Jain et al. (1999) as “the organization of a collection of patterns (usually represented as a
vector of measurements, or a point in a multidimensional space) into clusters based on similarity”.
Clustering is an advanced statistical method used to represent relationships, based on measures of
proximity between elements, expressed by maximum distances between groups and minimal
distances within each group. It suggests an unsupervised classification and grouping of similar
objects into subsets that are determined by the clustering process itself. Implementation of clustering
in management sciences is common in numerous applications including studies related to computer
science and web information management (e.g., Choi and Peng, 2004; Adami et al., 2005; Chen &
Hsu, 2006), supplier clusters related to collaborative learning and risk management (Hallikas et al.,
2005), marketing issues (e.g., Malhotra and Peterson, 2001; Tsai and Chiu, 2004), and others. Each
of these cluster analyses requires a distinct determination of the clustering technique (i.e, choosing
the most suitable algorithm such as single linkage (nearest neighbor), complete linkage (furthest
neighbor), average (between groups) linkage, Ward’s method, or binary-positive approach) (Lorr,
1983; Aldenderfer and Blashfield, 1984).
We chose for the current study a clustering method characterized by the binary-positive approach,
where the distance is defined by a positive attribute index based on the presence (1) or absence (0)
of an attribute value for each case. The cluster dataset is a two dimensional matrix composed of
cases on one axis and features on the other. The entries of the matrix are either ‘1’, if the case has
the specific feature, or ‘0’, if the case lacks that feature. Based solely on the positive vector, two
indexes are calculated: the Pair Similarity Index (PSI) that measures similarity between cases, and
the Group Similarity Index (GSI) that measures similarity within a group of cases (Gelbard and
Spiegler, 2000; Gelbard et al., 2007).
In the research analysis of the political campaign, the entities for clustering were the individual eligible
voters and the attributes were their various traits and characteristics including age, gender,
neighborhood, profession, habits, etc, that qualified the clustering procedures. For each attribute we
defined a set of possible values and every voter was either assigned to this sub-attribute or not. We
started with the identification of clusters of voters based on geographic locations in order to create
homogeneous groups of citizens that have similar local oriented interests. Other groups were
clustered base on common demographics such as place of employment, similar professions, common
religious preferences or an interest in sports. Another powerful method to identify homogenous
groups of supporters is derived from analyzing historical voter behavioral trends which is part of the
knowledge base that the information management professional bring to every campaign. The
efficiency of the clustering that produces the profiles of the various groups depends heavily on the
quality of the learning process that is responsible for building and updating the knowledge structure.
There is a positive linear relationship between the effort assigned to the debriefing and archiving
process of past occurrences and the value of their contribution to the development of a good
mitigation plan to head off future similar events. Continuous learning is mandatory for constructing a
solid process responsible for the transformation of information into knowledge, which in turn is further
transformed into effective managerial tools. Voter segmentation is the basis of every successful
political marketing campaign because the candidate is not perceived equally by all voters (Newman,
1994). Clustering and profiling voters into homogeneous groups enables the marketing professionals
of the campaign to present a different aspect of the candidate for the different groups of voters,
hence, gaining a wider collection of supporters.
The impact of poor quality data and a lack of a good structure of knowledge in any organization are
manifested in increased cost, lower customer and employee satisfaction, and difficulties to set and
execute strategy (Redman, 1998). The impact of poor quality of data and a lack of a good structure of
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