diff --git a/point_pattern.py b/point_pattern.py
index 1b0a5cb..f36ac06 100644
--- a/point_pattern.py
+++ b/point_pattern.py
@@ -33,7 +33,9 @@ def read_geojson(input_file):
     """
     # Please use the python json module (imported above)
     # to solve this one.
-    gj = None
+    with open(input_file,'r') as file:
+      gj = json.load(file)
+      print(gj)
     return gj
 
 
@@ -56,8 +58,13 @@ def find_largest_city(gj):
     population : int
                  The population of the largest city
     """
-    city = None
+    FeaturesGJ = gj['features']
     max_population = 0
+    for featureEntry in FeaturesGJ:
+        if featureEntry["properties"]["pop_max"] > max_population:
+            max_population = featureEntry["properties"]["pop_max"]
+            city = featureEntry["properties"]["nameascii"]
+
 
     return city, max_population
 
@@ -74,7 +81,13 @@ def write_your_own(gj):
     Do not forget to write the accompanying test in
     tests.py!
     """
-    return
+    location = gj['features']
+    unitedstates = 0
+
+    for i in location:
+        if i['properties']['adm0name'] == 'United States of America':
+            unitedstates += 1
+    return unitedstates
 
 def mean_center(points):
     """
@@ -93,8 +106,14 @@ def mean_center(points):
     y : float
         Mean y coordinate
     """
-    x = None
-    y = None
+    x = 0
+    y = 0
+
+    for point in points:
+        x += point[0]
+        y += point[1]
+    x = x / len(points)
+    y = y / len(points)
 
     return x, y
 
@@ -119,11 +138,20 @@ def average_nearest_neighbor_distance(points):
      Measure of Spatial Relationships in Populations. Ecology. 35(4)
      p. 445-453.
     """
-    mean_d = 0
-
+    nearest = []
+    for i, point in enumerate(points):
+         nearest.append(None)
+         for point2 in points:
+             if point is not point2:
+                 dist = euclidean_distance(point, point2)
+                 if nearest[i] == None:
+                     nearest[i] = dist
+                 elif nearest[i] > dist:
+                    nearest[i] = dist
+
+    mean_d = sum(nearest) / len(points)
     return mean_d
 
-
 def minimum_bounding_rectangle(points):
     """
     Given a set of points, compute the minimum bounding rectangle.
@@ -139,7 +167,24 @@ def minimum_bounding_rectangle(points):
        Corners of the MBR in the form [xmin, ymin, xmax, ymax]
     """
 
+    i = True
     mbr = [0,0,0,0]
+    for point in points:
+         if i:
+             i = False
+             mbr[0] = point[0]
+             mbr[1] = point[1]
+             mbr[2] = point[0]
+             mbr[3] = point[1]
+
+         if point[0] < mbr[0]:
+             mbr[0] = point[0]
+         if point[1] < mbr[1]:
+             mbr[1] = point[1]
+         if point[0] > mbr[2]:
+             mbr[2] = point[0]
+         if point[1] > mbr[3]:
+             mbr[3] = point[1]
 
     return mbr
 
@@ -148,7 +193,7 @@ def mbr_area(mbr):
     """
     Compute the area of a minimum bounding rectangle
     """
-    area = 0
+    area = (mbr[1] - mbr[3]) * (mbr[0] - mbr[2])
 
     return area
 
@@ -173,7 +218,7 @@ def expected_distance(area, n):
         The number of points
     """
 
-    expected = 0
+    expected = 0.5 * (area / n) ** 0.5
     return expected
 
 
diff --git a/tests/tests.py b/tests/tests.py
index 2518463..0b9bf4a 100644
--- a/tests/tests.py
+++ b/tests/tests.py
@@ -33,7 +33,7 @@ def test_write_your_own(self):
         point_pattern.py.
         """
         some_return = point_pattern.write_your_own(self.gj)
-        self.assertTrue(False)
+        self.assertTrue(some_return, 70)
 
 class TestIterablePointPattern(unittest.TestCase):
     """