SPATIAL TEMPORAL ANALYSIS OF ROAD NETWORK DENSITY IN ILE-IFE METROPOLIS, NIGERIA

Abstract

The road network density of Ile- Ife is assessed using a Geographic Information System (GIS). Global Navigation Satellite System (GNSS) receiver was used to acquire primary data, secondary data used for this study include; Topographical maps covering the study area for 1968, Landsat 5 and 7 images of 1986, 2000 and 2007 respectively, satellite image (SPOT 5) and Google earth Image (SPOT 5) of 2014 and 2019 respectively. Information of the road was also gathered through proper interactions with dwellers who have stayed in the geographical area long enough. Road intersections, also known as nodes, were digitally represented as points, while roadways, also known as arcs, were represented as lines. The study employed a simple descriptive analysis to characterize the various road network types. The Alpha, Beta, and Gamma Indexes were used to assess the degree of connection within the road network. The road density was determined with respect to the road length per unit area. Result of the road connectivity revealed that street network analysis showed a complex network with beta values of 0.50 (1968), 0.54 (1986), 0.55 (2000), 0.55 (2007), 0.56 (2014) and 0.55 (2019) respectively. However, the alpha index showed that the street network in all the years considered for this research were not perfectly connected as a negative value of alpha index were obtained and these were -0.25 (1968), -0.23 (1986), -0.23 (2000), -0.22 (2007), -0.22 (2014) and 0.22 (2014) %. Gamma index of Ile-Ife metropolis for the five years were 0.17, 0.18, 0.18, 0.18, 0.19 and 0.18. This implied that as much as 83, 82, 82, 82, 81 and 82% gaps respectively were needed to have a complete link within the network. The 2 2 2 2results of road density for each year varied from 6km/ km (1968), 11.97km/ km (1986), 13.93km/ km (2000), 11.9 km/ km 2 2(2007), 12.05km/ km (2014), 9.08km/ km (2019). These studies provide crucial empirical backing for planners and policymakers to comprehend how road network density affects mobility performance and create urban road networks that are more efficient.