(doc) add inline docstring for the equation of L function

doc/_static/references.bib

@@ -1,13 +1,26 @@
 %% This BibTeX bibliography file was created using BibDesk.
 %% https://bibdesk.sourceforge.io/
 
-%% Created for weikang at 2019-11-20 20:52:19 -0800 
+%% Created for weikang at 2020-04-22 14:18:22 -0700 
 
 
 %% Saved with string encoding Unicode (UTF-8) 
 
 
 
+@inbook{Sullivan2010,
+	Author = {O'Sullivan, D. and Unwin, D. J.},
+	Booktitle = {Geographic Information Analysis},
+	Chapter = {5},
+	Date-Added = {2020-04-22 14:18:04 -0700},
+	Date-Modified = {2020-04-22 14:18:21 -0700},
+	Doi = {10.1002/9780470549094.ch5},
+	Pages = {121-156},
+	Publisher = {John Wiley \& Sons, Ltd},
+	Title = {Point Pattern Analysis},
+	Year = {2010},
+	Bdsk-Url-1 = {https://doi.org/10.1002/9780470549094.ch5}}
+
 @article{Baker:2004,
 	Author = {Baker, Rose D},
 	Date-Added = {2019-11-20 20:48:20 -0800},

pointpats/distance_statistics.py

@@ -265,7 +265,7 @@ def __init__(self, pp, intervals=10, dmin=0.0, dmax=None, d=None):
 
 class L(DStatistic):
     """
-    Estimates the l function for a point pattern.
+    Estimates the :math:`L` function for a point pattern :cite:`Sullivan2010`.
 
     Parameters
     ----------
@@ -287,6 +287,27 @@ class L(DStatistic):
                  The distance domain sequence.
     l          : array
                  L function over d.
+
+    Notes
+    -----
+
+    In the analysis of planar point processes, the :math:`L` function
+    is a scaled version of :math:`K` function. Its estimate is also
+    typically compared to the value expected from a process that displays
+    complete spatial randomness (CSR):
+
+    .. math::
+
+            L(d) = \\sqrt{\\frac{K(d)}{\\pi}}-d
+
+    where :math:`K(d)` is the estimator for the :math:`K` function
+    and :math:`d` is distance.
+
+    The expectation under the null of CSR is 0 (a horizonal line at 0).
+    For a clustered pattern, the empirical :math:`L`
+    function will be above the expectation, while for a uniform pattern the
+    empirical function falls below the expectation.
+
     """
     def __init__(self, pp, intervals=10, dmin=0.0, dmax=None, d=None):
         res = _l(pp, intervals, dmin, dmax, d)