The decision to contract with a vendor or outsource for the automation of parcel maps should depend upon the organization need for the dataset, abilities to produce it in-house, and the available budget. The benefits of outsourcing may include overall lower costs. The cost of parcel automation ranges from $1.20 to $5.00 per parcel for manually digitized vector data (New York 1996, 48). Even if cost were not an issue, a municipality must ask itself whether it has the staff, will, and ability to produce a dataset at the level of quality desired. If the answer is no, municipalities may issue requests for proposal (RFP) and evaluate the proposals submitted by various contractors.
10.1 REQUEST FOR PROPOSALS 10.2 PERFORMING THE CONVERSION IN-HOUSE
Individuals preparing an RFP should structure the document to allow contractors to propose an approach that meets the data quality and budgetary requirements of the municipality. Some of the criteria that may be used in the selection of a parcel map conversion contractor include: the company's technical capability, experience with parcel automation, ability to communicate a technical plan of operation, personnel experience, and range of services (New York 1996, 49). The following outline details the parts of an RFP for parcel map data conversion: (For more detailed examples, see Appendix 1 which contains a sample RFP.)
A. Contract Information
· Instructions for labeling and preparation of proposal materials
· Number of copies
· Expiration date and time
· Bond percentage
· Contact person and address
B. Executive Summary
· Background of the organization
· Fundamental of the proposal in non-technical language
· Description of expected uses and benefits
C. Organizational Inventory
· Description current and/or future digital infrastructure
· Description current and/or future records data
· Description of parcel data end users
D. Statement of Work
· Nature of needs
· End-product specification
· Integration of the product into existing or future system
· Check plots
· Metadata
· Technical support, if necessary
E. Qualifications of Contractor
· Description
· Project summaries of similar work performed for other local governments
· References
· Resumes of staff involved
· Subcontractor information (if any)
F. Proposal Instructions
· Provide information regarding contractor's qualifications
· Describe in detail project procedures, implementation, schedules, etc.
· Explain how end users' needs will be met
· Describe strategies for integration in current or future system
G. Selection Criteria
· Format of the review and time schedule
· Firm qualifications
· Ability to meet needs of the project
· Reasonableness of cost and time estimates
H. Miscellaneous
· Waivers, disclaimers, liabilities
· Certificate of state tax complianceSource: Struck 1998, np
As a reference for preparing an RFP and subsequent contract, municipalities may obtain the Guide to Contracting for Tax Mapping Services from the Commonwealth Department of Revenue, Division of Local Services, 51 Sleeper Street, Boston, MA 02205-9490, (617) 626-2300, http://www.state.ma.us/dls.
10.2. Performing the Conversion In-House
Local governments that decide they have the equipment and expertise in-house to conduct the conversion process face two options: to conduct the project incrementally over a number of years or within a short time frame (Struck 1998, np). An incremental approach is supportable if budgetary and personnel resources are limited. This allows personnel to schedule a portion of their time to work on the project and to learn new techniques as they go. The disadvantage of this is that high turnover among technicians is commonplace and this may present problems for the consistency of process and accuracy of the product. More importantly, the value of spatial data is based on its currency. The longer the process, the less the representative the final product is of reality and thus less valuable to a multitude of applications.
The other choice is to build financial resources and the expertise to complete the project within two to three years. Such an approach requires teamwork and the full support of top management. The project will be demanding and to meet accuracy goals there will be no margin for substandard work as a result of internal strife or a lack of the necessary technologies. The benefit however, is a quick return on investment from current and highly usable dataset. In-house teams should keep in mind that, "[the] most valuable data is not the data that required the most resources to convert. It is the data that is integral to the greatest number of applications throughout the duration of its useful life span. That life span begins the moment each element is converted, not at the end of the conversion project." (Struck 1998, np)
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