Many problems in operations research and in economics reduce to the finding of one or more points minimizing some function of the distance. The Euclidean distance is most commonly used: such a metric is often well suited to modeling problems, but—above all—it is simple and behaves most regularly with respect to mathematical properties. Anyhow, some problems are better modeled (at least from some points of view) by other metrics; e.g., by the rectilinear one. Therefore, it is useful to have estimates concerning the difference among solutions based on different choices of the distance function. In this note we give, hopefully, a contribution in this directio
Distance predicting functions are commonly used for estimating road distances in transportation netw...
In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broa...
International audienceThis research work presents an extension of the Tobler’s first law (TFL), that...
Many problems in operations research and in economics reduce to the finding of one or more points m...
One the earliest challenges a practitioner is faced with when using distance-based tools lies in the...
In analysis, a distance function (also called a metric) on a set of points S is a function d:SxS->R ...
This article investigates the selection of a distance measure in location modeling. While in the emp...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
International audienceOur goal is to establish a mathematical framework for the description of geogr...
We investigate the meaning of the mathematical properties of distances in the fields of geography an...
In this paper we provide an application-oriented characterization of a class of distance functions m...
Continuous location models are the oldest models in locations analysis dealing with the geometrical ...
Distances between demand points and potential sites for implementing facilities are essential inputs...
Some management science models require estimates of distances between points in a road network based...
International audienceWe investigate the meaning of the mathematical properties of distances in the ...
Distance predicting functions are commonly used for estimating road distances in transportation netw...
In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broa...
International audienceThis research work presents an extension of the Tobler’s first law (TFL), that...
Many problems in operations research and in economics reduce to the finding of one or more points m...
One the earliest challenges a practitioner is faced with when using distance-based tools lies in the...
In analysis, a distance function (also called a metric) on a set of points S is a function d:SxS->R ...
This article investigates the selection of a distance measure in location modeling. While in the emp...
Many learning algorithms rely on distance metrics to receive their input data. Research has shown th...
International audienceOur goal is to establish a mathematical framework for the description of geogr...
We investigate the meaning of the mathematical properties of distances in the fields of geography an...
In this paper we provide an application-oriented characterization of a class of distance functions m...
Continuous location models are the oldest models in locations analysis dealing with the geometrical ...
Distances between demand points and potential sites for implementing facilities are essential inputs...
Some management science models require estimates of distances between points in a road network based...
International audienceWe investigate the meaning of the mathematical properties of distances in the ...
Distance predicting functions are commonly used for estimating road distances in transportation netw...
In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broa...
International audienceThis research work presents an extension of the Tobler’s first law (TFL), that...