Count data is numerical or categorical? I am working on road network data (csv) and my data has 6 features. Two of these features are continues and three of them are categorical. One of the these features is number of lanes of street. This feature shows how many lanes a road has. For instance, a road has two lanes a road has 4 lanes and so on. Plus, following image shows a road which has three lanes (this is an image just to show what is my mean exactly and my data is csv file).

I have three questions as follows:

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*The number of lanes is categorical or numerical? in some resources I read count data are categorical and some resources categorized them as numerical!!!! could you please let me know number of lanes is categorical or numerical?


*What approaches you suggest to cluster a data with various data type (mixed quantitative and qualitative)?


*Can I transform the continues data to categorical data and then perform clustering analysis?

In a measurement theory sense, the count of the number of lanes is a ratio variable, since it is meaningful to interpret differences between numbers of lanes and ratios of numbers of lanes.  This means that you have the option of treating this variable either as a nominal, ordinal, or ratio variable in your statistical mode.  The choice of whether to treat the number-of-lanes as a categorical variables is a modelling choice that will depend on the degree of flexibility you wish to have in how the number-of-lanes might be statistically related to the outcome variable.  (It is not really helpful to ask if it is "numeric" --- that term largely refers to the way in which statistical computing packages store a variable, rather than referring to a measurement scale.)
For example, if you were to treat the number-of-lanes as a categorical variable, there will be no constraint on the effect of each category on the outcome.  Now, there is obviously an ordinal aspect to the number-of-lanes variable, with more lanes indicating a larger type of road, so you might decide you want to constrain the effects of the categories so that they obey some kind of ordinal (or perhaps even linear) effect size on the outcome variable.  This is a modelling choice you will need to make based on a combination of things; the a priori coherence of possible effects, the empirical data, diagnostic testing, etc.
