To address this issue we use a text-matching approach known as \u00ab\u00a0the love machine\u00a0\u00bb. Simply put, for a given facility from an outside source, we find the facility in the SNIS with the closest name. We then search the outside source for the facility whose name is closest to the name from SNIS. If we find the original facility name, we consider those two facilities a match and add the outside source\u2019s coordinate data to our merged dataset. Where possible, sources\u2019 geographic hierarchy are used to restrict matches to the correct province or zone de sant\u00e9.<\/p>\n\n\n\n
Synthetizing Geospatial data<\/h2>\n\n\n\n
The set of synthesized geodata from the sources matched by our love machine approach presents challenge number three. Namely, with multiple coordinate points for a facility, how do we determine that facility\u2019s most likely location? Here we implement a GPS selection algorithm that takes into account the number of coordinate points available, their relation to the health zone that contains the facility, and how they are clustered to determine outliers and select the location closest to all of the valid points.<\/p>\n\n\n\n
![](\"https:\/\/lh4.googleusercontent.com\/B5QHCFrrTRacJY26xROVRAeWUiqMYi0RJbDIQaZt3GdlY-wqsXaT5PL1EKNLcz1NSHu5RhRrG_Hwydv7Zav79-ENW1Tq2Jz5kg4lblrroEbic6ubhX65tlEATB6MMZDPjpDEn3BL\")
![](\"https:\/\/lh6.googleusercontent.com\/fnZeguzdZgDTzeeMXcIu1P9sitsOYOBRDrNVCloeN0vvVB8zon1eOE_pGpAAC42vzSUecva2-goX-Ue9ZRFRDP3fPnBk-OZ3KqXcN_CzMXPYBB-DRj_8Ubp27h02gZwTSGwa6xB4\")
For example, the health facility shown in the map on the left, Bashimikie Centre de Sant\u00e9 in Lomami, has three points in the health zone (red, blue, and green). However, one is noticeably further away from the other two. The algorithm recognizes this, classifies the red point as an outlier, and takes the midpoint of the other two points (yellow) as the new \u2018best\u2019 coordinate location.<\/p>\n\n\n\n
The facility on the right has 4 points considered to be within an acceptable distance from the health zone. However after computing each point\u2019s distance to the midpoint of the others, the algorithm considers the green point in the upper left corner to be an outlier and selects as the \u2018best\u2019 point the midpoint of the three others, represented by the yellow dot<\/p>\n\n\n\n
![](\"https:\/\/lh4.googleusercontent.com\/gh0eN7ouilLC_fiWErklj0gWrAit2rJhpnIruX66-ljYGrGHU6yq_sVbDQkGYE-VsBR0SYfZhUhJwiBkrg27MDn8hWFC5AIdyXlBhkNoubMJ0YlRtWXdKXLPoI_2fVOt27VqOFTs\")
Our data synthesis approach increases the percent of facilities in DRC with coordinate locations from 35% to 73%, more than doubling the location data contained in the SNIS. The gains were not uniformly distributed \u2013 unsurprisingly the biggest improvements came from areas where aid organizations and academics have been most active (and thus we have the most data), such as the provinces of Kasai Central, Kasai Oriental, and Tanganyika.<\/p>\n\n\n\n
![](\"https:\/\/lh4.googleusercontent.com\/z6e6mEvMcBFOV3eAsn2sVxM5ZvEAnQYnUAody6rRaQ08oQ8yAaU-pSAAuu3KomWuC-RlMSq2iPyvA7GMuOnX_eMVqa3okyBo1xBFv6J0zYj30dvbjYM4GWTDsqReToKE4nGcH2uP\")
Improving our knowledge about countries health infrastructures<\/h2>\n\n\n\n
Although we hope to have humans verify the matching process in the near future, quantitative analysis of the results suggests that the quality of the synthesized data is quite good. The histogram above shows that most facilities have two or more GPS data points contributing to its identified location. Furthermore, the average point that our GPS selection algorithm chooses is just 2 kilometers from the points identified in the matching process.<\/p>\n\n\n\n
Using Iaso, our geodata management platform, as a backbone, we were able to combine data from the DRC Ministry of Health and third party aid and academic organizations using a text matching and GPS selection approach to increase the share of health facilities in the national health information system with location information by more than 100%. This work highlights how the platform can be used to compile and synthesize data from different sources to make substantial improvements in how much we know about the country\u2019s health infrastructure. Better information in the hands of international actors and national policy-makers can make operations more efficient, strategy more effective, and improve the health of the Congolese people.<\/p>\n","protected":false},"excerpt":{"rendered":"
Data in the global health sector is frequently scarce or fragmented. Bluesquare\u2019s novel approaches can create an information environment where decision-making is better informed.<\/p>\n","protected":false},"author":1,"featured_media":7495,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","editor_plus_copied_stylings":"{}","footnotes":""},"categories":[146,143,139],"tags":[189],"class_list":["post-6146","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-health-supply-chain-logistics","category-one-health-surveillance","category-projects","tag-supply-chain-logistics"],"acf":[],"yoast_head":"\n