Simplifying Decision Trees

Simplifying Decision Trees

Summary and Critique by Michael Pouch

Summary:

J.R. Quinlan offers different techniques to help simplify the making of decision trees while at the same time maintaining their accuracy. The purpose of the study is to simplify decision trees by using four methods that help prove their fluency and the ability to apply the knowledge.

The author begins to explain that in order to build knowledge based systems, the ability to function at an expert level in some task does not necessarily confer a corresponding ability to articulate this know-how. Therefore, all this knowledge that the “knowledge engineer” produced, tends to state the engineer’s conclusions and reasoning in broad terms for the analysis. Thus, Quinlan examines four methods to help simplify the method going into creating decision trees. On the whole, three of the methods that Quinlan examines involve pruning the decision tree by replacing one or two subtrees with leaves. The fourth method that Quinlan examines reformulates the decision trees as a set of production rules.

The first method that Quinlan introduces is the method of Cost-Complexity Pruning.  After creating a large decision tree, we look at the subtrees that have one to zero nodes. To prune a tree, we look at these nodes that can be evaluated based on its classification accuracy with the other nodes. Next, we denote these nodes that are to be pruned because they are not focused on the overall validation of the tree. The basic idea of cost-complexity pruning is not to consider all pruned subtrees, but only those that are the “best of their kind” in a sense to be defined.

The second method introduced is Reduced Error Pruning. First, you prune the nodes that are classified as the subtrees and you remove them to make one leaf. Nodes are removed on the basis of choosing the node whose removal most increases the decision tree accuracy on the graph. Pruning process continues until further pruning is harmful.

The third method is called Pessimistic Pruning, where it’s based on a single pass algorithm on a True or False approach. At each step, we remove one rule, such that its removal brings the lowest valued node among all possible removals of the rule in the current tree.

The fourth method simplifies the production rules that go into decision trees. As decision trees classify a case and the leaves are satisfied along the path of conditions, examining the conditions or rules along the path could be generalized on the left side of the tree. Once generalize, there is a certainty factor that comes along the cases. To classify a case, you must find a rule that applies to it. If there is more than one, choose the rule with the higher certainty factor. Overall, this method is a condition-elimination strategy.

Overall, J.R. Quinlan intention is to suggest methods for simplifying decision trees without compromising their accuracy.

Critique:

Overall, I find the first three methods easy to interpret and visualized. The pruning methods are helpful due to their ability to narrow and scope the tree the way that fits the overall validation. When the goal is to produce a sufficiently accurate compact concept description, pruning is highly useful. However, there is a lot swapping of conditions throughout the process. In reaction, decision trees perform well if a few highly relevant attributes exist, but less so if many complex interactions are present.  Since most decision trees divide the leaf into mutually exclusive regions to represent a concept, in some cases the tree should contain several duplications of the same sub-tree to represent the classifier. To conclude, decision trees provide a framework to consider the probability and payoffs of decisions to help the analyst evaluate the likely course, however, it is impossible to plan for all contingencies when it comes to its outcomes. Though the process is simplified, there is no statistical backing if it actually works.

Reference:

Quinlan, J. (n.d.). Simplifying Decision Trees. Massachusetts Institute Of Technology Artificial   Laboratory. Retrieved from https://dspace.mit.edu/bitstream/handle/1721.1/6453/AIM- 930.pdf?sequence=2.

A Decision Tree Method for Building Energy Demand Modeling

Summary and Critique by Claude Bingham

A Decision Tree Method for Building Energy Demand Modeling 

Summary 

Energy consumption has been identified as a major factor in long-term building impact. Additionally, newer buildings have consumed more and more energy over time. To that end, the researchers in this project wanted to construct an accurate predictive model that would be able to estimate future energy use of buildings.

They chose decision tree methodology to create the predictive models. Regression methods were noted to be too complex for users with limited mathematical training; the researchers saw neural networks as 'black boxes,' unreplicable for some of the same reasons of regression. Building simulations cannot accurately predict building occupant behavior patterns and therefore can only estimate what a building's energy consumption could be in a statically situational environment. Decision trees, however, is relatively simple, can manipulate numerical and categorical data, and does not require much computation.

Decision trees use a flowchart-like structure to show hierarchy, status, and category of data. In this study, for example, a decision tree depicted the outside temperature, if a room was occupied, and whether the air conditioning was on because of those previous two factors. Based on the number of recorded occurrences of each possible variable state, energy use can be approximated for an individual room.

To verify the actual ability of such a model to create reliably accurate predictions, the research team used the C4.5 decision tree algorithm with open-source WEKA data-mining software. This pairing was chosen for their flexibility and ability to apply multiple types of data. The constructed model is then tested against predicted values. In this research study, the model was constructed to include six categorical variables and four numerical variables based on data collected from 80 buildings in six districts in Japan. The resulting value was set to be either 'HIGH' or 'LOW' energy use intensity.

The test model was able to correctly predict 92% of expected cases. The researchers noted that the confidence interval was 80%, too low to be consistently reliable and the model was miss-attributing variables at times. This was likely due to the size of the data set and limited variable hierarchy (also tied to data set size and variety).

Critique

This research benefited greatly from examining reasons for and against using various methodologies for predictive studies. The experiment was well-explained and well executed, with one exception. The sample size for the test data was too small. While the results were both reasonably accurate, and passably reliable, it shows decision trees are not downward scalable for smaller data samples. This methodology appears to work well with large data sets, but not smaller ones.

Predicting Recovery in Patients suffering from Traumatic Brain Injury by using admission variables and physiological data

Predicting Recovery in Patients Suffering from Traumatic Brain Injury By Using Admission Variables and Physiological Data: A Comparison between Decision Tree Analysis and Logistic Regression

Authors: Peter J.D. Andrews, M.D., F.R.C.A., Derek H. Sleeman, Ph.D., F.R.S. (ED), Patrick F.X. Statham F.R.C.S (Sn), Andrew McQuatt M. Sc., Vincent Corruble Ph.D., Patricia A. Jones, M. App. Sci., Timothy P. Howells, Ph.D., and Carol S. A. Macmillan, M.R.C.P., F.R.C.A

Summary:

This study compared results given by logistic regression and decision tree analysis on patients suffering from Traumatic Brain Injury. The researchers studied 124 patients in an intensive care unit over a 12 month period by using a data collection system. They input the values into a logistic regression, then created a decision tree from root nodes to target classes. This is called the Glasgow Outcome Score.

The researchers then assessed the outcome after 12 months of 69 patients with 8 insult or head injury categories. They input the data into a logistic regression to assess how patient age, Glasgow Coma Scale Score, Injury Severity Score, pupilary response upon admission, and insult duration. After they input the data, they found hypotensive, pyrexic, and hypoxemic insults to be the most significant predictors of mortality. 

After they used decision tree analysis, they found that hypotension and low cerebral perfusion pressure to be the most significant predictors of death. The analysis proved to 9.2 percent more accurate than just using the largest outcome category as a predictor of mortality.

Critique:

The article gave a good explanation for why decision tree makes for more accurate analysis. It also did a decent job of listing the variables used for the analysis. However, this is one study done on the decision tree analysis in the medical field and more specifically dealing with brain injuries. The study will need to be replicated using different numbers of patients to give an complete assessment of how accurate decision tree analysis is in that field.

Citation:

http://thejns.org/doi/pdf/10.3171/jns.2002.97.2.0326. 

Summary of Findings: Gap Analysis (2 Of 5 Stars)

Note: This post represents the synthesis of the thoughts, procedures and experiences of others as represented in the articles read in advance (see previous posts) and the discussion among the students and instructor during the Advanced Analytic Techniques class at Mercyhurst University in September 2017 regarding Gap Analysis as an Analytic Technique specifically. This technique was evaluated based on its overall validity, simplicity, flexibility and its ability to effectively use unstructured data.

Description:

Gap analysis is a methodology that allows one to estimate the “gap” in between the current situation of a company and a specific goal of the company. Operational gaps tend to look internally, while intelligence gap analysis looks at the external environment and what their competitors’ strategies are. This highlights a fundamental difference in what is meant by gaps, where intelligence gaps are essentially missing pieces of a puzzle, while operational gaps are related to obstacles to be overcome.  

Strengths:

• It helps to prepare preliminary list of gaps per scenario.
• Helps identify what areas to improve upon
• Essentially is an internal audit of the organization
• Gives a picture of “where you are” and “where you want to be” to provide estimates on how to close the gaps
• Help identify an objective and how to plan a course action

Weaknesses:

• Little to no evidence gap analysis is an effective methodology
• Gap analysis might generate misleading results, if the individuals carrying out the analysis do not conduct a proper trend analysis beforehand.
• Gap analysis doesn’t address complexities when analysis gaps
• Can lead to more operational based questions if not appropriately used
• Ignores the possibilities of biases if used incorrectly
• Typically will not be effective to use if company does not have the necessary resources
• 
  

How-To:

1. Identify the strategic goal.
2. Select most appropriate measure of potential gap (qualitative/quantitative).
1. Make a list of triggers to which led to gaps
3. Collect information about current state.
4. Analyze difference between current state and goal.
1. Compare situational difference between current state and strategic goal (qualitative).
2. Calculate quantitative difference between current state and strategic goal (quantitative).
3. Look at external environment in which the company is operating i.e. industry, competitors.
5. Discuss what further topics and questions need to be answered in order to reach desired goals.
6. Assign action for all gaps

Application of Technique:

To demonstrate the application of the Gap Analysis methodology, the class looked at an example case study depicting the gap in service quality between T-Mobile and AT&T. The class was instructed to research service coverage, plan prices, customer service, and product quality and compare qualitative or quantitative differences between the two service providers in those areas. Multiple areas showed both qualitative and quantitative areas with gaps. While going through the process, the analysis subtly changed from an intelligence exercise into theoretical operational brainstorming. Overall, while the analysis answered the initial question of what gaps existed between T-Mobile and AT&T, the methodology did not prove to be a valid way to increase analysis quality.

For Further Information:

1. Towards Total Product Quality Gap Analysis
2. Gap Analysis - Wikipedia
3. Gap Analysis Definition - Search CIO

GAP ANALYSIS REVISITED

Summary:

Gap analysis is a methodology, which is often used by various organizations and individuals seeking to reduce overall inefficiency or gaps in their respective environments. These gaps or inefficiencies may vary in their nature, scope and complexity, but the same analytical process can be applied to resolve them, if one chooses to use gap analysis. According to the article, Gap Analysis revisited, written by Chevalier in relation to the “International Society for Performance Improvement’s 10 Standards of Performance Technology”, the methodology consists primarily in establishing the existing and desired performance levels, then working to bridge the gap between the two by setting reasonable goals that will help achieve the desired outcome. According to the author, determining the existing and desired levels of performance is the starting point while conducting gap analysis. Nonetheless, the author stresses the fact that this it is primordial that this process be followed by the chartering of plausible targets for bridging the said gap. These “reasonable targets” he argues, must be broken down in smaller objectives and laid out in relevant and comprehensive terms to ensure that every participant involved in the exercise feels a sense of ownership, and thus is motivated and able to work towards achieving these targets. In the article, Chevalier provides us with an example that relates to this notion. In this example he mentions that the Netherlands swimming team decided to increase its performance level in the 100-meter free-style event from 51 seconds to near the world record of 47.84 seconds. To achieve this challenging but attainable goal, the team decided to reduce this gap of 3 seconds into smaller intermissions of .02 second a week and .004 second per training day respectively. This way the team was able to reduce the difference in their existing performance level of 51 seconds to 48 seconds which was their desired level of performance, by breaking down their overall target into smaller reachable objectives.

Ultimately, though it is evident that reducing the void between the existing and desired levels of performance and setting goals to that end is the essence of gap analysis, the author of the article argues that without well informed trend analysis, the outcome of conducting gap analysis might prove to be erroneous. To support this argument, Chevalier contends that depending on the nature of the existing performance level, the outcome of the gap analysis will either be misleading or correct. For example, he states that in a given organization, if there was an upward trend before bridging the gap, then continued upward performance may not necessarily be an indication that that the actions to increase efficiency added any value.

Critique:

Chevalier does a great job in demonstrating how one can apply gap analysis in order to improve performance levels in various fields. Nonetheless, there seems to be a major flow in his study of the analytical technique. Although it is clear that identifying “where you are” and “where you want to be” and setting objective to that end is the way to go, there seems to be a missing a link. It is great to carry out these steps, but how efficient are they if one has not determined the underlying problem which created the gap in the first place. For example, while trying to reduce their time in the Olympics from 51 to 48 seconds, did the Netherlands swimming team identify why they were below average to begin with? Therefore, the article should have included the process of discerning underlying problems in performance levels before targets are set to increase efficiency.

Source:

http://eds.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=14&sid=d05a93f9-a98c-445e-9eac-44ad0b5f9b1f%40sessionmgr4010

Applying Revised Gap Analysis Model in Measuring Hotel Service Quality

 Summary and Critique by Evan Garfield

Summary

The authors of this study proposed a revised gap model to evaluate and improve service quality in the Taiwanese hotel industry. The gap analysis method involves defining the present state, the desired target, and the gap in between. Gap analysis aims to look at ways to bridge the gap through backward chaining logical sequences. This method was used as a method in hopes of offering better direction in developing and improving service quality.

 The authors argue that hotel organizations have difficulty in adequately assessing and improving service performance from a customers' perspective. Organizations struggle in identifying which factors customers consider most important with regards to service quality. The authors explain that although the gap model has its limitations, it remains the leading measure of service quality. Gap analysis in the hotel industry with regards to service quality traditionally focuses on customer perceptions of service vs. expectations. With gap analysis, management gains an understanding of customer expectations. This insight helps influence design, development, and delivery of the service.

The authors argue, however, that evaluation of service quality should not only be based on customer perceptions, but also employees, managers, and service providers. Gap analysis of service quality perceptions vs. expectations from the perspectives of customers, service providers, and managers provides valuable insight into needed improvement at multiple levels within the organization. The aim of this study was to explore gaps at these additional levels.

The study found 5 gaps influenced tourists' evaluations of service quality. Findings revealed that Gap 1 (management perceptions vs. customer expectations) and Gap 9 (service provider perceptions of management perceptions vs. service delivery) were more critical than other gaps in affecting perceived service quality, highlighting service delivery as the main area of improvement to increase service quality.

The authors provided a revised Gap conceptual model (pictured below) to better understand and measure gaps of service quality. The model provides a functional relationship, illustrating the combination of gaps and the decomposition of service activity.

Critique
 This study illustrates how gap analysis can be effectively applied in problem solving. The study uses gap analysis to gain a better understanding of the current state (perception) vs. the desired state (expectations) at multiple levels within the service delivery hierarchy. Traditional research focused exclusively at the gap between customer perception and expectation. This study revealed overlooked gaps at other levels influencing service quality (employee, managerial, service provider, etc.) The authors also provide a helpful conceptual model to illustrate the connection and combination of multiple gaps.
In complex problems,  there are multiple gaps between the desired state and current states. This study illustrates that gap analysis should not be merely perceived as 1 broad gap (current state vs. desired state). Aiming to bridge 1 broad gap through back-chaining logistical sequences is messy and difficult. Gap analysis should rather break down each gap into multiple gaps in order to facilitate more effective problem solving. By breaking down the current state and the desired state into different pieces (gaps), it easier to understand micro-level problems and the necessary steps needed to bridge those gaps to meet ones goal (desired state).



Source: Applying Revised Gap Analysis Model in Measuring Hotel Service Quality
 http://eds.a.ebscohost.com.ezproxy.mercyhurst.edu/ehost/pdfviewer/pdfviewer?vid=13&sid=37ae6589-674c-4a68-b36f-315a8edb8e80%40sessionmgr4010

Gap Analysis: An Innovative Look at Gateway Courses and Student Retention

Summary and Critique by Keith Robinson Jr.

Summary

Authors William Bloemer, Scott Day, and Karen Swan of University of Illinois--Springfield challenged the notion that identifying gateway courses in which faculty focuses their attention on the large number of students that fail or withdraw is an effective use of limited resources. The authors recognized that these gateway courses do not have the same impact on all students, an unfair assumption that previous approaches in quantifying and identifying the best "fix" tend to share. Students come from diverse backgrounds and have different learning needs. They argue that the effectiveness of a course should be determined by the contexts of the students within it; "it is unreasonable to expect all courses to serve students equally well." The authors searched for the right measures to identify problem gateway courses, taking a look at the context of the students enrolled.

The data was pulled from all undergraduate degree-seeking students enrolled in a small, Midwestern public university over a four year period. An end of term grade of D, F, or prior Withdrawal from a course indicated that the student failed to complete the course successfully. The authors measured persistence, enrollment in the next regularly scheduled term or graduation, for two reasons: the probability of a student that takes a break in enrollment to graduate is much lower because most students simply do not return, and in order to connect individual courses to persistence it must be done with a short measure.

Students were classified according to type and stages in their academic life cycle. Student types were as follows: Native Freshmen, Honors Freshmen, On-ground Transfers, and Online Transfers. Stages in the academic life cycle were the first term (critical for transfer students), end of the first year/second semester (freshmen), and second and third year. Anything beyond the third year was considered the last stage. The research utilized a binary logistic regression to predict the probability a student would post a D, F, or Withdraw grade in any specific course the institution offered over a particular four-year period. Student Type, point in the Student Life Cycle, prior cumulative GPA, and fraction of courses students already received a D, F, or W were predictor variables. The predicted D, F, and W rates were utilized as a benchmark against actual course performance, and the difference (Gap) between them was calculated.

The results of the study illustrated that the ranking of courses based on actual and predicted DFW rates painted a similar picture; however, significant differences were noted as well. Some courses with high DFW rates had high predicted rates, some courses performed better than expected. There were also courses with DFW rates not high enough to attract attention but are much higher than expected given the student population. And there were courses with extremely low DFW rates, near zero, despite predicted rates being substantial.

In conclusion, while gateway courses with D, F, and Withdrawal rates are the first to receive the attention of retention efforts, at times this attention is misdirected and often harmful. It may be necessary to identify "problem" courses to replicate its relative success with a specific student type or problematic student population. It is beneficial to identify problem courses, however, it is equally as beneficial to identify the student types and at what cycle they are in their academic careers to base expectations.

Critique

The study is clearly limited to the undergraduate population of a small, Midwestern public university over a four-year period; it is not indicative of all students across the US. In analysis of D, F, and W grades received, there appeared to be students with high GPAs with high W rates, assumed to protect their GPAs. That skews the data. It is also nearly impossible to truly quantify problem courses. While a course may prove difficult, it could just as easily be due to lack of student effort. Additionally, The authors recognize that other approaches than that utilized in the research may be more appropriate for other academic institutions based on local factors that impact student success (or failure). And finally, while the fastest, most cost-effective solution to high DFW rates may be in academic advising such as preventing particular types of students from attempting courses known to be difficult for students at their current stage of academic life cycle, that may come across as discriminatory. 

Source: 
Gap Analysis: An Innovative Look at Gateway Courses and Student Retention

Gap Analysis: A Simple Tool For Achieving Your Business Goals By Ott Niggulis

Written by Oddinigwe Onyemenem

Summary

According to the author, gap analysis addresses two questions in relation to the present state “where are we” and the target state “where we want to be” of a business. Gap analysis has various names used to describe it; however, its core principle is to find solutions to problems that are holding back a business. The process of conducting a gap analysis relies on the objectives of the business embarking on it.  The process involves comparing current/actual performance with planned and/or desired performance. Furthermore, a gap analysis can be performed by evaluating results in comparison to industry averages to find gaps in performance relative to competition. Niggulis points out the tendency to attempt to close every identified performance gap which will be a mistake. He suggests prioritizing the gap by focusing on the gap that has the most immediate impact on the business.

The article identifies three main steps for conducting a gap analysis which is prefaced by identifying the area of focus for the analysis. The first step is identifying the current and future states which involve taking an in depth look at where the present business results and/or performance and the most desired result and/or performance. The second step involves identifying and describing the gap. Niggulis suggests using the “five-whys analysis” tool to narrow down the possible causes of the identified gap. The third step is bridging the gap. It is important to note that there may be costs involved in implementing the necessary steps to close the identified gap. A properly executed gap analysis gives a business the ability to identify problems, the causes of the problems, and possible solutions to go from the present state to the target state. 

Critique

Niggulis explains gap analysis in a clear and concise manner. The identified steps are easily practicable for any business size. The article focuses on the main objective of a gap analysis, which is to get a business from where it is to where it wants or can be. Gap analysis provides an objective overview of the state of a business for decision makers. There is no concern for bias and it helps to uncover underlying issues that exist.  In examining the areas of focus, it is important to remain consistent and avoid straying into areas that are out of scope.

The article fails to address any complexities that may be encountered by a business in conducting the gap analysis. In a situation where the implementation costs to bridge the gap for a business is way beyond the financial or human capabilities of the business, will the business shift its focus to less expensive gaps which will in turn result in less impact for the business or abandon the process? Some insights relating to such complexities that may arise will be beneficial to address.  Overall, the article provides easy-to-follow steps and explanations about conducting a gap analysis for a business.

Source: https://www.shopify.com/enterprise/102475782-gap-analysis-a-simple-tool-for-achieving-your-business-goals

Gap Analysis as it Relates to IT

"A Gap Analysis Process to Improve IT Management" by John Murray

Summarized by: Sam Farnan

Gap analysis is a straightforward yet practical method for businesses to improve minor functions and set short-term goals. This method is useful also for internal affairs, such as IT. Efforts to further streamline and improve IT departments often become entangled in complexity and lack a concrete set of objectives, ultimately costing much more than they should. Gap analysis provides a quick and inexpensive method for small to medium sized efforts within an IT department to solve problems, acheive goals, increase efficiency.

Gap analysis as a process stipulates that to be utilized correctly:

• Goals must be identified
• Analysis of current methods, processes, and procedures
• A realistic plan to close the gap between the current state and the goal
• Potential hang-ups and obstacles are identified and mitigated
• The plan is followed, and the desired goal is attained

Despite the overall simplicity of the process, it does have limitations. Murray states that the process cannot be substituted for complete re-engineering or a restructuring of an IT organization. The scale of the problem should not encompass a large host of problems, but smaller ones instead. Unlike an expensive restructuring, a cheaper and quicker gap analysis is best used on less difficult issues. Murray writes that gap analysis is a tiered process. Specifically, when one small goal is met, the next one is immediately set and pursued. This, he states, allows for a continuing string of successes. 

He applies this method to incremental goal setting--such as increasing project delivery rates--to phased development within the IT organization. Murray emphasizes that all parties involved must not get ensnared in the critical step of the process: obstacle identification. Whether in a small or large group setting, cooperation and realistic expectations are key to the success of this method. Finally, to keep the process honest, an audit should be conducted to ensure goals were met and identify any improvements for future application. 

Critique:

Murray lays out at a basic level how gap analysis can be utilized in a difficult area: IT. Murray does not discount the reality that restructuring is inevitable and useful when correctly applied, but he highlights the low cost to high benefit ratio of a well-utilized gap analysis. However, his optimism as it relates to IT merging with other departments of an organization to set achievable goals is slightly unrealistic. Technology doesn't always work when it should--hence the need for IT--but more importantly, the bureaucratic process often is the elephant in the room that no one wants to address. However, Murray emphasizes at numerous points in his piece that gap analysis is best applied to small to medium sized problems, not overarching organizational flaws. This methodology is simple in both theory and practice, but it does not allow for cognitive bias, as misreading or completely ignoring the reality of an obstacle will likely hinder any real goal a team wishes to achieve. 

Sources:

"A Gap Analysis Process to Improve IT Management" by John Murray

Gap Analysis Today: A Confluence Of Biology, Ecology, And Geography For Management Of Biological Resources

Summary and Critique by: Ian Abplanalp

Summary

Michael D. Jennings in his article took a problem of how to better assess the distribution and conservation efforts. Jennings proposed a gap analysis of a multi-state conservation analysis (confusingly called GAP). Jennings focused on improving the conservation analysis, by determining the external forces that shaped natural resource management of GAP as it exists presently, the direction as well as the goals of GAP, then defined how the results of the gap analysis could be utilized.

Jennings first assed the five factors shaping natural resource management of GAP. The primary factor discussed was the rise in environmental change now taking place, and at a rate much higher than previously seen before. The second factor is the adoption and application of holistic models for the management of natural resources. Thirdly Jennings, addresses an increase of technology advancements in planning, research, that provided larger amounts of raw data that can be disseminated in new ways. The fourth factor is an ever changing political and social climate. The last factor is how the previous four factors are changing the way that resource professionals and their institutions are conducting business. Jennings also illustrates that along with these factors a systematic approach to ecological information across large areas of the GAP program are lacking.

After laying out where GAP stands, Jennings lays down a pair of goals. The first of which would be to "identify areas critical for the protection of biological resources". This would help identify  conservation statuses of different plants and animals in varying areas. The second goal is to use the knowledge gained through the first goal to leverage a larger group effort to target these areas deemed most critical. Jennings outlines that this would take a large investment from personal member of the conservation community to put aside differences and work collaboratively to best protect the environment.

Jennings, the develops what would be an ideal path for the conservation community to take. GAP would need to move in a more unified direction among the conservation community. This would be aided through the new and improved way to generate and disseminated data, through mapping. The conservation community would also like to decrease the discrepancies of accuracy, and scale of conservation efforts amongst themselves by putting aside past differences. GAP would also like to include more interest in their program by incorporating more states to participate within the program. These steps would help improve the unity of GAP and further aid conservation practices.

Jennings, lastly outlines how the results of a gap analysis could be utilized in a manner to more efficiently handle conservation efforts. The results could be used in three ways the first of which is that professional resource planners and land managers could use the data provided to more accurately assess areas in their everyday work. Secondly agencies could apply results to new conservation lands or changing management of current conservation lands. Third is that planners can take a larger statewide approach to looking at biodiversity management.

Critique

Jennings overall shows how useful and adaptive gap analysis can be to many different situations and fields. Jennings shows the diversity of gap analysis methodology, through stretching it from its business roots and applying it to an ecological problem. Jennings makes this methodology applicable to a multitude of field by using it only by identifying where the conservation economy is and where it would like to go. Spreading to more than one field makes this methodology a powerful tool as anyone can take an honest look at where they are, were they would like to go, and whats missing in-between. The methodology is also a useful tool to use that it can be applied mid-process to adjust a project to better answer questions, or give guidance to an entirely new problem.

The methodology is limited in that only individuals that have information readily available to them about factors that are influencing their current state. For example if someone uses this methodology without knowing the proper factors or misjudging which factors are influencing their situation will cause them to potentially miss their goal. Also if no clear goal is established there will not be an accurate way to assess the steps needed to be taken after determining which forces are influencing one's situation.

Sources

Gap analysis today: A confluence of biology, ecology, and geography for management of biological resources