Completed

analytics.py

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+import utils
+import math
+
+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
+    """
+    city = None
+    max_population = 0
+
+    features = gj['features']
+    for i in features:
+        if(i['properties']['pop_max']>max_population):
+            max_population = i['properties']['pop_max']
+            city = i['properties']['name']
+
+    return city, max_population
+
+def write_your_own(gj):
+    """
+    Here you will write your own code to find
+    some attribute in the supplied geojson file.
+
+    Take a look at the attributes available and pick
+    something interesting that you might like to find
+    or summarize.  This is totally up to you.
+
+    Do not forget to write the accompanying test in
+    tests.py!
+    """
+
+    #I chose the least creative route of finding the average population among all cities
+
+    sum_population = 0
+    n = 0
+
+    features = gj['features']
+    for i in features:
+        sum_population = i['properties']['pop_max']+sum_population
+        n = n+1
+
+    return sum_population/n
+
+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
+    """
+    x = 0
+    y = 0
+    n = 0
+
+    for i in points:
+        x = x+i[0]
+        y = y+i[1]
+        n = n+1
+
+    x = x/n
+    y = y/n
+    return x, y
+
+
+# def average_nearest_neighbor_distance(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.
+#     """
+
+#     #create empty list of distances
+#     shortestDistList = []
+
+#     for i in points:
+#         shortest = 9999999999
+#         for j in points:
+#             if i!=j:
+#                 current = euclidean_distance(i,j)
+#                 if(current<shortest):
+#                     shortest = current
+#         shortestDistList.append(shortest)
+
+#     n = 0
+#     mean_d = 0
+#     for i in shortestDistList:
+#         mean_d = mean_d+i
+#         n = n+1
+
+#     return mean_d/(n)
+
+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]
+    """
+
+    mbr = [0,0,0,0]
+
+    x_max = -99999999999
+    x_min = 999999999999
+    y_max = -99999999999
+    y_min = 999999999999
+
+    for i in points:
+        if i[0] > x_max:
+            x_max = i[0]
+        if i[0] < x_min:
+            x_min = i[0]
+        if i[1] > y_max:
+            y_max = i[1]
+        if i[1] < y_min:
+            y_min = i[1]
+
+        mbr = [x_min,y_min,x_max,y_max]
+
+    return mbr
+
+def mbr_area(mbr):
+    """
+    Compute the area of a minimum bounding rectangle
+    """
+    area = (mbr[3] - mbr[1]) * (mbr[2] - mbr[0])
+
+    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 compute_critical(points):
+    """
+    Compute the "critical" points for the Monte Carlo
+    simulation as the minimum and maximum of the points
+
+    Parameters
+    ----------
+    points : float
+           The area of the study area
+
+    (min,max) : int
+        The minimum and maximum list
+    """
+
+    return min(points), max(points)
+
+def check_significant(input_min,input_max,X):
+    """
+    Compute the "critical" points for the Monte Carlo
+    simulation as the minimum and maximum of the points
+
+    Parameters
+    ----------
+    area : float
+           The area of the study area
+
+    n : int
+        The number of points
+    """
+
+    flag = False;
+
+    if X>input_max:
+        flag = True
+    elif X<input_min:
+        flag = True
+
+    return flag

io_geojson.py

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+import json  # I would like you to use the JSON module for reading geojson (for now)
+
+
+def read_tweets(inputFile):
+    with open(inputFile, 'r') as fp:
+        tweets = json.load(fp)
+    return tweets
+
+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.
+
+    gj = None
+
+    with open(input_file, 'r') as f:
+        gj = json.load(f)
+    return gj