9/11/2023 0 Comments Heidelberg germany floodIn total 1.36 million inhabitants were affected by an increase in travel time to the nearest hospital due to indirect effects of the flood event from up to 5 minutes to 5 to 10 minutes and 140,000 by an increase from 5 to 10 travel time to 10 to 15 minutes. Indirectly affected were those areas which still have access during a disaster compared to the normal scenario, but which required a longer journey time to the nearest hospital. Directly affected were those areas which were actively flooded and therefore no longer passable – in total 2.75 million inhabitants were living in those areas. The flooding had direct as well as indirect impact on accessibility. Under normal conditions almost the entire area of the city of Jakarta had access to at least one hospital or one clinic within a 10 to 15 minute car drive. The dashed lines represent the mean of the distribution: grey: normal, black: flooded – for betweenness centrality the two lines cannot be distinguished due to the small difference in mean. Histogram of BC and HC values for normal conditions and the flood event. On average the HC value decreased from 0.25 to 0.13. Strongest losses in HC value occurred in nodes located near the city border. More than 60 percent of the remaining 111,379 nodes changed their HC value by less than -/+0.2, which indicates that most of the nodes and the major part of the network, respectively, maintained its functionality regarding fast access. Most of the nodes (~75 percent) did not change in value or only changed by a small amount of -/+0.0005, which means that these nodes had an increasing or decreasing traffic volume of up to 0.05 percent.Įffects of the flood event on HC were a bit more pronounced than for BC. A few nodes in the center of the city gained in betweenness centrality but on average the value decreased slightly from 0.00004 to 0.00002. Effects of the flood event on betweenness centrality where relatively small. 34,027 nodes, which were flooded and therefore no longer passable. Nearly a quarter of all nodes and 25 percent of the edges were affected by the flood event, which split the network into one main graph and several subgraphs. Nodes with a higher closeness rating can therefore be regarded as important supply points as well as locations which can be reached quickly by all other nodes. In contrast to the BC, the results of the HC indicate which nodes can be reached most quickly on average for all existing nodes. Nodes with a higher HC are closer to all other nodes and can therefor be reached by others best. Since the classic formula cannot be used for disconnected graphs the harmonic closeness centrality was used. A doubling in value implies that the node is passed twice as often.Ĭloseness centrality (CC) counts the reciprocal of the average distance from a specific node to any other nodes within the network. An even spatial distribution of betweenness values indicates a resilient road network, since the maintenance of traffic does not depend on individual nodes and is therefore not affected by the failure of individual sections. Nodes with a high betweenness value are crossed more frequently and are in this context important for a fast routing and traffic flow. Since travel times were used as weights, the BC measured the frequency at which the respective nodes were approached or crossed on the fastest routes. The higher the betweenness centrality value of a node, the more important the node in the network. OSM contributions for road network and health sites in Jakarta.īetweenness centrality (BC) denotes the fraction of shortest paths which pass through the focal node between all node pairs. Bed capacity was available for ~60% of the hospitals and ~54% of the clinics. In addition to OSM data flood data – kindly provided by HOT Indonesia – was used.Īn intrinisc data quality analysis based on the ohsome API revealed a sufficient completeness for road infrastructure and clinics and hospitals. The analysis was based on two connectivity indicators (betweenness centrality and harmonic closeness centrality) as well as on the isochrones functionality of the openrouteservice. Isabell Klipper studied how the 2013 flood in Jakarta, Indonesia affected the connectivity of the road network and accessibility to clinics and hospitals. Low-income countries have a death rate 23 times higher than countries with high financial resources and are therefore much more affected by the impacts. Floods are the most common natural disaster worldwide, responsible for economic, social and life losses. Due to climate change and anthropogenic activities, the number and intensity of these events has steadily increased at the global scale. Extreme natural events create catastrophic situations for cities and their populations.
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