Provider: Pitney Bowes Business Insight
Enhance geographic data analysis and visualization in MapInfo Professional with Vertical Mapper and the power of grids. Vertical Mapper works seamlessly with MapInfo Professional to display your data in a continuous surface-a grid-with data representation at any given cell on the surface, regardless of boundaries. Use Vertical Mapper to visualize elevation, show the distribution of wealth across a city, optimize the location of broadcast towers, analyze health issues, model environmental information and much, much more!
Vertical Mapper imports a number of formats that represent some of the more commonly distributed grid types. The majority of these file types are grids that represent the elevation of a land surface, more commonly referred to as a digital terrain model (DTMs) or digital elevation models (DEMs).
- ASCII Classified
- Geological Survey of Canada
- UK Ordinance Survey
Inverse Distance Weighting (IDW) Interpolator
The IDW method is an averaging technique that produces highly smoothed surfaces. The original data points are not honoured and, therefore, calculated values can never be higher than a local maximum or lower than a local minimum.
Triangular Irregular Network (TIN) Interpolator
This interpolation method honors the value at each data point and performs some degree of over/under estimation between data points.
This method of interpolation honors each data point without over/undershooting values between data points. Although processing time is extremely short on any data set, it tends to produce reasonable results only on data sets with regularly space point distribution. This generally occurs when importing raw grid information from other sources.
Natural Neighbor Interpolator
This flexible interpolator is not limited by the distribution of points in a data set and gives you the option to perform over/undershooting of values between data points. The Natural Neighbor method also honors each point in the set. Another notable feature is that it does not limit the number of data points used in the calculation of each grid node. Instead, it creates a natural neighbor region for each point and calculates the grid node from every region that touches it.
Kriging is a geo-statistical interpolation method that considers both the distance and the degree of variation between known data points when estimating unknown areas. It also provides the ability to create semi-variograms that help users understand directional (e.g., north-south, east-west) trends of their data. A unique feature of kriging is the error estimation for each grid node which gives a measure of confidence in the modeled surface. Several different variations of kriging are available including Ordinary, Simple, Universal and Block Kriging, as is the ability to apply a nugget effect when doing multi-directional analysis of the point file.
The Location Profiler modeling tool generates a grid by averaging the distance to all point locations within a given search radius to determine the value of each grid node. By identifying those areas that are the shortest average distance to all or some of the points, the grid can be used to represent geographic centre(s) of activity. The model can be further refined to take into account point weighting and distance decay functions that define the relative influence of each data point.
Trade Area Analysis
This tool applies the Huff Model to identify trade areas around one or more store locations. When generating a grid, each grid node is considered to be the location of a potential customer. The calculation involves estimating the probability that a customer at that site (grid node) will patronize a particular store. There are three main variables used in the calculation:
- the distance to each store within a given search radius,
- the distance decay curve that weights each store with respect to their distance to the grid node, and
- an attractiveness value that weights the store with respect to its quantitative and/or qualitative characteristics (e.g., number of parking spaces, product selection, and customer service).
Region to Grid
Perform complex queries than cannot be done in vector-based GIS systems by using the Region to Grid tool to convert a MapInfo region file to a grid. The process extracts a value (text or numeric) from a column in the region table and assigns this value to all grid cells that fall inside the region. If the assigned value is numeric, a numeric grid is created. Similarly, if the information taken from the column is a text string, a classified grid is automatically created and all color attributes of the region file are transferred to the grid.
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Since 1993, Spatial Insights has been leveraging unique partnerships with the industry’s leading providers of mapping software and data to provide solutions tailored specifically to meet our clients’ diverse needs.
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