The use of census migration data to approximate human movement patterns across temporal scales

Abstract

Human movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data.

Figure 1. A comparison of the ranked estimates of movement.

Counties in Kenya are colored according to the total outgoing rank from A) mobile phone derived movement data (the number of trips between 2 and 3 months, for example movements relevant for studying infectious diseases where transmission varies seasonally, such as influenza) and B) census derived migration data. The actual values are shown in C) with the one-to-one x-y line shown in red. D) The percentage of the population moving between all pairs of counties. For each movement variable, absolute outgoing movements were weighted by the percentage of the population moving to each destination. For both census migration data and mobile phone movement data (the number of trips between 2–3 months), a ranked value was calculated (adjusted R-squared = 0.5421, p<0.001).

Figure 2. The distribution of trip durations between counties from mobile phone derived movements and census derived migrations.

The probability of a trip of various distances for both the census-derived migration data and mobile phone usage data (number trips lasting between 2 and 3 months) was calculated.

Figure 3. Gravity-type spatial interaction model fits for the mobile phone usage data.

Gravity models were calibrated for each movement variable. A) The parameter values for are shown from the fit for various trip durations. Each parameter value from the census data is shown in the corresponding color as a dotted line. A gravity model was calibrated to fit the number of trips between counties lasting between 2 and 3 months. B) The actual data versus the gravity model fit is shown in the figure (Data/Fit). The ratio of true data to the results of the fitted model are shown broken down by C) population at the origin county, D) population at the destination and E) the distance (in kilometers) between the origin and destination. The model underestimates movements from low population counties (both as an origin and destination) and shorter trips.