CROWD-SOURCING DATA AND QUALITY CONTROL: OSM ROAD VALIDATION IN LOW INCOME COUNTRIES

Transcription

1 CROWD-SOURCING DATA AND QUALITY CONTROL: OSM ROAD VALIDATION IN LOW INCOME COUNTRIES World Data System Webinar #8 Paola Kim-Blanco & Bogdan- Mihai Cîrlugea CIESIN, Columbia University February 10, 2016.

2 HOW ACCURATE AND HOW COMPLETE* IS OSM s ROADS DATASET IN LOW INCOME COUNTRIES? SHOULD WE ASSIMILATE OSM ROADS INTO GLOBAL ROADS V2? * We refer to spatial coverage of road features

3 PREVIOUS VALIDATION WORK Positional accuracy and completeness of road features are among the most common quality parameters tested by researchers. Both parameters have been tested against authoritative or proprietary datasets. Most cases reviewed were conducted at the national or city level from developed countries. Results have been varied, though they generally favor OSM. Presence of urban bias. Few examples on how to assess OSM data quality without using other reference datasets. We did not find evidence of predicting missing roads through spatial regressions or classification methods.

4 STRATEGY 4 WA countries: Guinea, Ghana, Liberia and Senegal. Population (GPW4), relative wealth (DHS circa 2012) and populated places (GeoNames.org) as predictors of road density (OSM, Oct 2015). Unit of analysis is admin level 2 Develop a series of test diagnostics that can give a sense of overall accuracy and completeness of road features in OSM. POSITIONAL ACCURACY COMPLETENESS Calculate RMSE against Google Earth as ground truth in randomly selected junctions, controlling for urban/ rural classification. 3 methods: Discrete classification Spatial regression Connecting settlements

5 POSITIONAL ACCURACY RMSE in all 4 cases is below 50 meters, which means that on average positional accuracy is below groads v1 target. Three out of four countries had average RMSE of less than 15 meters in both urban and rural areas, which is pretty remarkable! could that be groads v2 target? (mmm )

6 DISCRETE CLASSIFICATION ROAD DENSITY POP DENSITY/ RELATIVE WEALTH LIBERIA Areas with relative lower road density Areas with relative high population density and relative wealth Areas were flagged when these 2 conditions matched

7 CLASSIFICATION RESULTS SENEGAL GHANA GUINEA Flagged areas where road density is below median; and population density and relative wealth is above median We observed areas with missing roads in all flagged units in Google Earth --yay! But we also noticed areas with missing roads that were not flagged grrrr

8 LIBERIA Roads tagged as paths (5% of all road segments in Liberia, btw ) These areas were not caught by the classification method Tobler s 1 st law of geography: Everything is related to everything else, but closer things more so then, could we expect more roads next to areas with high road densities? Let s test it with a Durbin spatial model!

9 SPATIAL DURBIN MODEL EX. LIBERIA May be? Areas with highest prediction respect to current road status Areas with some predicted roads missing Could be?

10 YEAH, BUT THEN 43% false positive units in Ghana, respect to GE 11% false positive units in Guinea & 30% false positive units in Liberia 0% false positive units.. But then the scale of admin level 2 in Senegal does not work for this type of analysis

11 CONNECTING SETTLEMENTS If roads connect settlements, may be unconnected settlement points may give us another indication of areas with unmapped roads? Input used now: GeoNames.org As next step, we ll test AfSIS GeoSurvey geosurvey.qed.ai Because we did not like these artifacts in the data SEARCH ROADS WITHIN A RADIUS OF 1 KM 2.5 KM 5 KM 10 KM

12 With this method it is easy to identify clear gaps in the roads network! GUINEA

13 CONCLUSIONS In all 4 countries, the positional accuracy of OSM roads is within an acceptable range. In terms of completeness There is no one method that gives an absolute and truthful result about road coverage in a country but the combination of the 3 can give a good idea. Limitations: modifiable areal unit problem, quality of data inputs, cut- off values, among many others GUINEA Classification Spatial prediction Connectivity among settlements