diff --git a/analytics.py b/analytics.py
new file mode 100644
index 0000000..f355ad1
--- /dev/null
+++ b/analytics.py
@@ -0,0 +1,249 @@
+'''
+Created on Feb 23, 2016
+
+@author: Max Ruiz
+'''
+
+import math
+
+'''
+Function List
+-------------
+find_largest_city(gj)
+mean_center(points)
+average_nearest_neighbor_distance(points)
+minimum_bounding_rectangle(points)
+mbr_area(mbr)
+expected_distance(area, n)
+euclidean_distance(a, b) # also in utils.py
+
+compute_critical(p)
+check_significant(lower,upper,observed)
+'''
+
+'''Assignment 5 functions'''
+
+def compute_critical(p):
+    """
+    Given a list, p, of distances (constants), determine the upper and lower
+    bound (or max and min value) of the set. The values in p are assumed floats.
+
+    Parameter(s): list p
+
+    Return(s): float lower, float upper
+    """
+    lower = min(p)
+    upper = max(p)
+    return lower, upper
+
+def check_significant(lower, upper, observed):
+    """
+    Check if given observed point is outside or within a given lower and upper
+    bound.
+
+    Parameter(s): float lower, float upper, float observed.
+
+    Return(s): boolean
+    """
+    return observed < lower or observed > upper
+
+def find_largest_city(gj):
+    """
+    Iterate through a geojson feature collection and
+    find the largest city.  Assume that the key
+    to access the maximum population is 'pop_max'.
+
+    Parameters
+    ----------
+    gj : dict
+         A GeoJSON file read in as a Python dictionary
+
+    Returns
+    -------
+    city : str
+           The largest city
+
+    population : int
+                 The population of the largest city
+    """
+
+    max_population = 0
+    for feat in gj['features']:
+        test_max_pop = feat['properties']['pop_max']
+        if test_max_pop > max_population:
+            max_population = test_max_pop
+            city = feat['properties']['name']
+
+    return city, max_population
+
+def mean_center(points):
+    """
+    Given a set of points, compute the mean center
+
+    Parameters
+    ----------
+    points : list
+         A list of points in the form (x,y)
+
+    Returns
+    -------
+    x : float
+        Mean x coordinate
+
+    y : float
+        Mean y coordinate
+    """
+    sumx = 0.0
+    sumy = 0.0
+    for coord in points:
+        sumx += coord[0]
+        sumy += coord[1]
+    x = sumx / len(points)
+    y = sumy / len(points)
+
+    return x, y
+
+
+def average_nearest_neighbor_distance_tuples(points):
+    """
+    Given a set of points, compute the average nearest neighbor.
+
+    Parameters
+    ----------
+    points : list
+             A list of points in the form (x,y)
+
+    Returns
+    -------
+    mean_d : float
+             Average nearest neighbor distance
+
+    References
+    ----------
+    Clark and Evan (1954 Distance to Nearest Neighbor as a
+     Measure of Spatial Relationships in Populations. Ecology. 35(4)
+     p. 445-453.
+    """
+    min_dist_sum = 0
+    for coord_n in points:
+        first = True
+        for  coord_m in points:
+            if coord_n == coord_m:
+                continue
+            else:
+                d = euclidean_distance(coord_n, coord_m)
+                if first:
+                    min_dist = d
+                    first = False
+                else:
+                    if d < min_dist:
+                        min_dist = d
+        min_dist_sum += min_dist
+
+    mean_d = min_dist_sum / len(points)
+
+    return mean_d
+
+def average_nearest_neighbor_distance(points, mark = None):
+    if mark != None:
+        pointsWithMark = list()
+        for x in range(len(points)):
+            if points[x].getMark() == mark:
+                pointsWithMark.append(points[x].getPoint())
+            else:
+                continue
+        return average_nearest_neighbor_distance_tuples(pointsWithMark)
+    else:
+        allPoints = list(points[x].getPoint() for x in range(len(points)))
+        return average_nearest_neighbor_distance_tuples(allPoints)
+
+def minimum_bounding_rectangle(points):
+    """
+    Given a set of points, compute the minimum bounding rectangle.
+
+    Parameters
+    ----------
+    points : list
+             A list of points in the form (x,y)
+
+    Returns
+    -------
+     : list
+       Corners of the MBR in the form [xmin, ymin, xmax, ymax]
+    """
+    xmin = 0
+    xmax = 0
+    ymin = 0
+    ymax = 0
+    for coord in points:
+        if coord[0] < xmin:
+            xmin = coord[0]
+        elif coord[0] > xmax:
+            xmax = coord[0]
+
+        if coord[1] < ymin:
+            ymin = coord[1]
+        elif coord[1] > ymax:
+            ymax = coord[1]
+
+    xcorner = xmax - xmin
+    ycorner = ymax - ymin
+    mbr = [0,0,xcorner,ycorner]
+
+    return mbr
+
+
+def mbr_area(mbr):
+    """
+    Compute the area of a minimum bounding rectangle
+    """
+    length = mbr[3] - mbr[1]
+    width = mbr[2] - mbr[0]
+    area = length * width
+
+    return area
+
+
+def expected_distance(area, n):
+    """
+    Compute the expected mean distance given
+    some study area.
+
+    This makes lots of assumptions and is not
+    necessarily how you would want to compute
+    this.  This is just an example of the full
+    analysis pipe, e.g. compute the mean distance
+    and the expected mean distance.
+
+    Parameters
+    ----------
+    area : float
+           The area of the study area
+
+    n : int
+        The number of points
+    """
+
+    expected = 0.5 * math.sqrt(area / n)
+    return expected
+
+def euclidean_distance(a, b):
+    """
+    Compute the Euclidean distance between two points
+
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+
+    distance : float
+               The Euclidean distance between the two points
+    """
+    distance = math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
+    return distance
diff --git a/io_geojson.py b/io_geojson.py
new file mode 100644
index 0000000..efa1028
--- /dev/null
+++ b/io_geojson.py
@@ -0,0 +1,33 @@
+'''
+Created on Feb 23, 2016
+
+@author: Max Ruiz
+'''
+import json
+
+'''
+Function List
+-------------
+read_geojson(input_file)
+'''
+
+def read_geojson(input_file):
+    """
+    Read a geojson file
+
+    Parameters
+    ----------
+    input_file : str
+                 The PATH to the data to be read
+
+    Returns
+    -------
+    gj : dict
+         An in memory version of the geojson
+    """
+    # Please use the python json module (imported above)
+    # to solve this one.
+
+    with open(input_file,'r') as f:
+        gj = json.load(f)
+    return gj
diff --git a/point.py b/point.py
new file mode 100644
index 0000000..3c32240
--- /dev/null
+++ b/point.py
@@ -0,0 +1,41 @@
+'''
+Created on Mar 15, 2016
+
+@author: Max Ruiz
+'''
+
+
+class Point(object):
+
+    def __init__(self, x, y, mark = None):
+        self.x = x
+        self.y = y
+        self.mark = mark
+
+    def __add__(self, other):
+        return Point(self.x + other.x, self.y + other.y)
+
+    def __eq__(self, other):
+        return self.x == other.x and self.y == other.y
+
+    def __neg__(self):
+        return Point(-self.x, -self.y)
+
+    def check_coincident(self, point):
+        return (self.x == point[0] and self.y == point[1])
+
+    def shift_point(self, x_shift, y_shift):
+        self.x += x_shift
+        self.y += y_shift
+
+    def getx(self):
+        return self.x
+
+    def gety(self):
+        return self.y
+
+    def getPoint(self):
+        return (self.x, self.y)
+
+    def getMark(self):
+        return self.mark
diff --git a/point_pattern.py b/point_pattern.py
new file mode 100644
index 0000000..159471e
--- /dev/null
+++ b/point_pattern.py
@@ -0,0 +1,92 @@
+
+
+from point import Point
+import analytics
+import utils
+import random
+import numpy as np
+
+class PointPattern(object):
+    def __init__(self):
+        self.points = []
+
+    def add_point(self, point):
+        self.points.append(point)
+
+    def remove_point(self, index):
+        try:
+            del(self.points[index])
+        except:
+            pass
+
+    def average_nearest_neighbor_distance(self, mark=None):
+        return analytics.average_nearest_neighbor_distance(self.points, mark)
+
+    def num_of_coincident(self):
+        numCoin = 0
+        accounted = []
+
+
+        for i in range(len(self.points)):
+            for j in range(len(self.points)):
+                if i in accounted:
+                    continue
+                if i == j:
+                    continue
+                if self.points[i] == self.points[j]:
+                    numCoin += 1
+                    accounted.append(j)
+
+        return numCoin
+
+    def list_marks(self):
+        marks = []
+        for point in self.points:
+            if point.mark != None and point.mark not in marks:
+                marks.append(point.mark)
+        return marks
+
+    def find_subset_with_mark(self, mark):
+        marked_points = []
+        for points in self.points:
+            if points.mark == mark:
+                marked_points.append(points)
+        return marked_points
+
+    def generate_random_points(self, n=None):
+        if n is None:
+            n = len(self.points)
+        rndmPoints = []
+        self.marks = ['burrito', 'chimichanga', 'steak', 'burger', 'chillidog',
+                 'sweetpotatofries', 'beans', 'bacon', 'beijingbeef', 'friedeggs',
+                 'icecream', 'brownies', 'cookie', 'bananasplit', 'almondjoy']
+
+        for i in range(n):
+            rndmPoints.append(Point(round(random.random(), 2), round(random.random(), 2), random.choice(self.marks)))
+        return rndmPoints
+
+    def generate_realizations(self, k):
+        return utils.permutations(k)
+
+    def get_critical_points(self):
+        return analytics.compute_critical(self.generate_realizations(100))
+
+    def compute_g(self, nsteps):
+        discStep = np.linspace(0, 1, nsteps)
+        sum = 0
+        for i in range(nsteps):
+            i_step = discStep[i]
+            minDist = -1
+            for j in range(len(discStep)):
+                if i == j:
+                    continue
+                if minDist == -1:
+                    minDist = abs(discStep[j] - i_step)
+                else:
+                    if abs(discStep[j] - i_step) < minDist:
+                        minDist = abs(discStep[j] - i_step)
+                    else:
+                        minDist = minDist
+            sum += minDist
+
+        return sum / nsteps
diff --git a/test_point_pattern.py b/test_point_pattern.py
new file mode 100644
index 0000000..89c8436
--- /dev/null
+++ b/test_point_pattern.py
@@ -0,0 +1,38 @@
+
+import unittest
+#from point_pattern import PointPattern
+#from point import Point
+
+from point_pattern import PointPattern
+from point import Point
+
+
+class TestPointPattern(unittest.TestCase):
+    def setUp(self):
+        self.point_pattern = PointPattern()
+        self.point_pattern.add_point(Point(1, 2, 'burrito'))
+        self.point_pattern.add_point(Point(2, 1, 'burrito'))
+        self.point_pattern.add_point(Point(1, 2, 'burger'))
+        self.point_pattern.add_point(Point(1, 2))
+
+    def test_coincident(self):
+        self.assertEqual(self.point_pattern.num_of_coincident(), 2)
+
+    def test_list_marks(self):
+        self.assertEqual(self.point_pattern.list_marks(), ['burrito', 'burger'])
+
+    def test_find_subset_with_mark(self):
+        self.assertEqual(len(self.point_pattern.find_subset_with_mark('burrito')), 2)
+        self.assertEqual(len(self.point_pattern.find_subset_with_mark('burger')), 1)
+
+    def test_generate_random(self):
+        self.assertEqual(len(self.point_pattern.generate_random_points()), 4)
+        self.assertEqual(len(self.point_pattern.generate_random_points(10)), 10)
+
+    def test_generate_realizations(self):
+        self.assertEqual(len(self.point_pattern.generate_realizations(100)), 100)
+
+    def test_compute_g(self):
+        self.assertAlmostEqual(self.point_pattern.compute_g(10), 0.111, places=3)
+        self.assertAlmostEqual(self.point_pattern.compute_g(100), 0.010, places=3)
+        self.assertAlmostEqual(self.point_pattern.compute_g(1000), 0.001, places=3)
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..899230f
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,213 @@
+'''
+Created on Feb 23, 2016
+
+@author: Max Ruiz
+'''
+import math
+import random
+#import analytics
+from analytics import average_nearest_neighbor_distance
+from point import Point
+
+'''
+Function List
+-------------
+manhattan_distance(a, b)
+euclidean_distance(a, b)
+shift_point(point, x_shift, y_shift)
+check_coincident(a, b)
+check_in(point, point_list)
+getx(point)
+gety(point)
+
+create_random(n)
+permutations(p)
+'''
+
+
+def create_random_marked_points(n, marks=[]):
+    random.seed()
+    randPoints = list()
+    for x in range(n):
+        _x = random.randint(0,100)
+        _y = random.randint(0,100)
+        if marks is None:
+            randPoints.append(Point(_x, _y))
+        else:
+            if len(marks) == 0:
+                randPoints.append(Point(_x, _y))
+            else:
+                rndmark = random.choice(marks)
+                randPoints.append(Point(_x, _y, rndmark))
+    return randPoints
+
+def permutations(p=99, n=100, marks = None):
+    """
+    Calculate p number of average_nearest_neighbor_distances from n number
+    of randomly generated points. Return list of size p with distance values.
+
+    Parameter(s): integer p, integer n
+
+    Return(s): list perm
+    """
+    perm = []
+    for x in range(p):
+        points = create_random_marked_points(n, marks)
+        avg_nnd = average_nearest_neighbor_distance(points)
+        perm.append(avg_nnd)
+
+    return perm
+
+def manhattan_distance(a, b):
+    """
+    Compute the Manhattan distance between two points
+
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+    distance : float
+               The Manhattan distance between the two points
+    """
+    distance =  abs(a[0] - b[0]) + abs(a[1] - b[1])
+    return distance
+
+
+def euclidean_distance(a, b):
+    """
+    Compute the Euclidean distance between two points
+
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+
+    distance : float
+               The Euclidean distance between the two points
+    """
+    distance = math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)
+    return distance
+
+
+def shift_point(point, x_shift, y_shift):
+    """
+    Shift a point by some amount in the x and y directions
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    x_shift : int or float
+              distance to shift in the x direction
+
+    y_shift : int or float
+              distance to shift in the y direction
+
+    Returns
+    -------
+    new_x : int or float
+            shited x coordinate
+
+    new_y : int or float
+            shifted y coordinate
+
+    Note that the new_x new_y elements are returned as a tuple
+
+    Example
+    -------
+    >>> point = (0,0)
+    >>> shift_point(point, 1, 2)
+    (1,2)
+    """
+    x = getx(point)
+    y = gety(point)
+
+    x += x_shift
+    y += y_shift
+
+    return x, y
+
+
+def check_coincident(a, b):
+    """
+    Check whether two points are coincident
+    Parameters
+    ----------
+    a : tuple
+        A point in the form (x,y)
+
+    b : tuple
+        A point in the form (x,y)
+
+    Returns
+    -------
+    equal : bool
+            Whether the points are equal
+    """
+    return a == b
+
+
+def check_in(point, point_list):
+    """
+    Check whether point is in the point list
+
+    Parameters
+    ----------
+    point : tuple
+            In the form (x,y)
+
+    point_list : list
+                 in the form [point, point_1, point_2, ..., point_n]
+    """
+    return point in point_list
+
+
+def getx(point):
+    """
+    A simple method to return the x coordinate of
+     an tuple in the form(x,y).  We will look at
+     sequences in a coming lesson.
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    Returns
+    -------
+     : int or float
+       x coordinate
+    """
+    return point[0]
+
+
+def gety(point):
+    """
+    A simple method to return the x coordinate of
+     an tuple in the form(x,y).  We will look at
+     sequences in a coming lesson.
+
+    Parameters
+    ----------
+    point : tuple
+            in the form (x,y)
+
+    Returns
+    -------
+     : int or float
+       y coordinate
+    """
+    return point[1]