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nccos/BiogeographicAssessments_TBNMSPrioritizationResults (MapServer)

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NCCOS used an on-line application to gather opinions from 24 local experts regarding their priorities for mapping the lakebed within TBNMS. The system allowed respondents to indicate where mapping is needed, the types of map data that are required, the urgency of the need, and a rationale to justify their priorities. Based on analysis of the responses, a few groups of cells emerged as the highest overall priority for mapping. These were primarily located within 20 km of shore and were concentrated between Alpena and Rogers City. They had the highest overall coin totals, a large number of respondents per cell, and a similar mix of justifications and desired map data types base on the cluster analysis (Cluster 3). Several additional areas emerged as highly important for certain groups or data needs.

What caused these patterns of high priority cells? Plotting the data in a diversity of ways allowed us to disentangle the various priorities among experts from different fields. We not only identified important areas that were unique to each group, but perhaps more importantly, we also identified areas that are a high priority for more than one field of experts.

v 1.20200610



Map Name: Thunder Bay NMS Spatial Prioritiation Results

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NCCOS used an on-line application to gather opinions from 24 local experts regarding their priorities for mapping the lakebed within TBNMS. The system allowed respondents to indicate where mapping is needed, the types of map data that are required, the urgency of the need, and a rationale to justify their priorities. Based on analysis of the responses, a few groups of cells emerged as the highest overall priority for mapping. These were primarily located within 20 km of shore and were concentrated between Alpena and Rogers City. They had the highest overall coin totals, a large number of respondents per cell, and a similar mix of justifications and desired map data types base on the cluster analysis (Cluster 3). Several additional areas emerged as highly important for certain groups or data needs.

What caused these patterns of high priority cells? Plotting the data in a diversity of ways allowed us to disentangle the various priorities among experts from different fields. We not only identified important areas that were unique to each group, but perhaps more importantly, we also identified areas that are a high priority for more than one field of experts.

v 1.20200610



Copyright Text: NOAA/NOS/NCCOS/Marine Spatial Ecology

Spatial Reference: PROJCS["Mercator_1SP",GEOGCS["GCS_WGS_1984",DATUM["D_unknown",SPHEROID["WGS84",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",0.0],PARAMETER["standard_parallel_1",0.0],UNIT["Meter",1.0]]


Single Fused Map Cache: false

Initial Extent: Full Extent: Units: esriMeters

Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP

Document Info: Supports Dynamic Layers: true

MaxRecordCount: 2000

MaxImageHeight: 4096

MaxImageWidth: 4096

Supported Query Formats: JSON, geoJSON, PBF

Supports Query Data Elements: true

Min Scale: 0

Max Scale: 0

Supports Datum Transformation: true



Child Resources:   Info   Dynamic Layer

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