# load packages from h3 import h3 import geopandas as gpd import geopandas.tools from shapely import geometry, ops from shapely.geometry.polygon import Polygon from shapely.geometry import Point import pandas as pd import numpy as np import matplotlib.pyplot as plt from scipy.stats import nbinom import statsmodels.api as sm from numba import njit import matplotlib.colors as mcolors from bokeh.io import show from bokeh.plotting import figure import bokeh.models as bm from bokeh.models import LinearColorMapper, FixedTicker from bokeh.palettes import RdBu
Who controls territory in civil war? This is a central variable in the research and analysis of civil wars – yet it is incredibly difficult to measure. In this post, I model territorial control as a latent variable – an unobserved variable that presumes it is the cause of its indicators. In other words, the data we have is the effect of the latent variable. Latent variables differ from most empirical research in that we often implicitly assume effect indicators; indicators are the effect of the variable being measured. Latent variables are basically the disease-symptom model of phenomena: the disease causes the symptoms, not vice versa.
Upfront, the fruits of latent variable modeling are shown below. I use a Hidden Markov Model to predict territorial control sequences and their predicted probability on a country-wide scale using Uber’s hexagonal spatial indexing.