https://andres-chamorro.com Andres Chamorro Fri, 26 Mar 2021 04:51:45 +0000 https://andres-chamorro.com en https://andres-chamorro.com/Home Tue, 12 Sep 2017 01:17:04 +0000 Andres Chamorro https://andres-chamorro.com/Home https://andres-chamorro.com/About Sat, 16 Sep 2017 07:10:32 +0000 Andres Chamorro https://andres-chamorro.com/About I’m a geographer currently working for the World Bank’s Geospatial Operations Support Team. I’m passionate about leveraging the unique data resources of today to tackle complex social and environmental issues. I’m a nimble programmer with a strong foundation in remote sensing, spatial econometrics, and a good eye for organizing data in compelling layouts. In the past, I managed a unique dataset used to forecast food insecurity risk in conflict-ridden areas (Famine Action Mechanism), and provided GIS support to an innovative platform producing deforestation alerts from satellite imagery (Global Forest Watch). In my free time, I enjoy rock climbing and watching soccer (always rooting for the underdog). https://andres-chamorro.com/Urban-Exposure-Floods Fri, 26 Mar 2021 04:51:45 +0000 Andres Chamorro https://andres-chamorro.com/Urban-Exposure-Floods The following python notebook quantifies exposure to floods in the Caribbean islands. Below is a technical description of the data processing workflow. <img width="2200" height="1700" width_o="2200" height_o="1700" data-src="https://freight.cargo.site/t/original/i/08a2855cd9b25f930656bc05311cad476554c056fbc30595f6f7022a981fc462/Urban-Built-up-Absolute.jpeg" data-mid="103313962" border="0" src="https://freight.cargo.site/w/1000/i/08a2855cd9b25f930656bc05311cad476554c056fbc30595f6f7022a981fc462/Urban-Built-up-Absolute.jpeg" /> <img width="3060" height="1809" width_o="3060" height_o="1809" data-src="https://freight.cargo.site/t/original/i/80a9673325f82a119c278ee26cfd9bc86fa582b01913d798f7b5350621c91b56/Imagery-and-Flood.png" data-mid="103312709" border="0" src="https://freight.cargo.site/w/1000/i/80a9673325f82a119c278ee26cfd9bc86fa582b01913d798f7b5350621c91b56/Imagery-and-Flood.png" /> <img width="3060" height="1809" width_o="3060" height_o="1809" data-src="https://freight.cargo.site/t/original/i/4ce671fcc0075193d08aa01499b1fd5211a2b3ef046a16fe98a2b2a2ad565287/WSF-and-Floods.png" data-mid="103309794" border="0" src="https://freight.cargo.site/w/1000/i/4ce671fcc0075193d08aa01499b1fd5211a2b3ef046a16fe98a2b2a2ad565287/WSF-and-Floods.png" /> <img width="3060" height="1809" width_o="3060" height_o="1809" data-src="https://freight.cargo.site/t/original/i/4fa8c93805b480f86d182e4da72bef365b27404efcaaa040b55d8a70357f8ac8/WSF-and-Flood.png" data-mid="103312894" border="0" src="https://freight.cargo.site/w/1000/i/4fa8c93805b480f86d182e4da72bef365b27404efcaaa040b55d8a70357f8ac8/WSF-and-Flood.png" /> <img width="3060" height="1809" width_o="3060" height_o="1809" data-src="https://freight.cargo.site/t/original/i/d96b10db447148d637e551c27b602df42c2874a2ab70c7abd0ce826700fe0e46/WSF-and-Flood.png" data-mid="103313016" border="0" src="https://freight.cargo.site/w/1000/i/d96b10db447148d637e551c27b602df42c2874a2ab70c7abd0ce826700fe0e46/WSF-and-Flood.png" /> The first part of the script assembles raster files from various sources (flood data, built-up evolution, and population) to create one single virtual raster stack for each country. One of the biggest challenges was setting up a workflow to clean and process monstrously disorganized flood data. The data is complex for two reasons: (1) flood depth is estimated for different sources of flooding (fluvial, pluvial, or coastal), and different scenarios of flood intensity (return period), and (2) the technical properties of the data were inconsistent across islands. The key workaround here was to build GDAL commands to merge flood tiles and project them into one raster file, then subsequently stack them along with other exposure data via more GDAL magic (BuildVRT). I was blown away by how well this last command worked. It just worked! And the spatial resolution of the data? Originally, I wanted to preserve the highest resolution available from all the datasets (30-meter), but in the end I settled on the resolution of the population dataset (~100-meter) to avoid up-sampling population. The virtual raster by itself does not preserve information about the order in which the raster bands have been stacked. Some countries had incomplete datasets, so the order wasn’t consistent across all VRTs. To account for this, I implemented a simple dictionary to keep track of each band index and what dataset it contained. The second part of this script loops through the different flood scenarios and intersects exposure data to calculate zonal statistics. The results store the number of urban cells exposed to floods and the number of people exposed to floods, at different points in time. The first version of this script quickly crashed when it tried to load a multi-band raster for the Bahamas. It was just too much data to load at once. This prompted me to tweak the implementation and use urban extents as the unit of analysis, using rasterio’s window reading to avoid issues with memory. Results would then be aggregated to the country-level at a later step, making use of a pandas MultiIndex to keep track of the flood scenario characteristics. https://andres-chamorro.com/Famine-Action-Mechanism Mon, 18 Feb 2019 00:07:23 +0000 Andres Chamorro https://andres-chamorro.com/Famine-Action-Mechanism The Big Picture The Famine Action Mechanism is a World Bank-led intiative that seeks to eradicate famine by strenghtening the links between early warnings, financing, and implementation arrangements. My Role My main contribution has been to create and mantain a time-series dataset that combines granular information of the problem (food insecurity outcomes) with key predictors (agronomic stress, conflict, and market disruptions).&nbsp; This dataset is currently used by our team to model the risk of future food crises, providing direct insights to FAM for better preparadness and earlier release of funds. Work Samples- Computing Monthly Weather Statistics (Google Earth Engine)- Spatial Interpolation of Market Prices (R)- Overlay of Conflict Events and Food Insecurity in Yemen <img width="2000" height="1400" width_o="2000" height_o="1400" data-src="https://freight.cargo.site/t/original/i/5a21e2f07a72214512c85191ecaef54ebbc48fa33ee52710c5599f1ed889d717/Cadale.jpeg" data-mid="42160671" border="0" src="https://freight.cargo.site/w/1000/i/5a21e2f07a72214512c85191ecaef54ebbc48fa33ee52710c5599f1ed889d717/Cadale.jpeg" /> <img width="1700" height="2200" width_o="1700" height_o="2200" data-src="https://freight.cargo.site/t/original/i/7bfe31107460ce48ef83355c3bc31fa7fd233b9a3fefae9db3d8cd247a935c43/Ethiopia-Weather.jpeg" data-mid="37461630" border="0" src="https://freight.cargo.site/w/1000/i/7bfe31107460ce48ef83355c3bc31fa7fd233b9a3fefae9db3d8cd247a935c43/Ethiopia-Weather.jpeg" /> <img width="2000" height="1400" width_o="2000" height_o="1400" data-src="https://freight.cargo.site/t/original/i/61455cb82c886f3e7c899a3cc69f14f39faa9cef28a6ccbb965916020fb2cb4e/Frequency_all.jpeg" data-mid="37393971" border="0" src="https://freight.cargo.site/w/1000/i/61455cb82c886f3e7c899a3cc69f14f39faa9cef28a6ccbb965916020fb2cb4e/Frequency_all.jpeg" /> <img width="2000" height="2000" width_o="2000" height_o="2000" data-src="https://freight.cargo.site/t/original/i/840279b43f3e9a5cd1b9cf6516cdd8647dae7f2a2e4106618e543f6c0106a734/Somalia-IPC-Time-Series.jpeg" data-mid="37461637" border="0" src="https://freight.cargo.site/w/1000/i/840279b43f3e9a5cd1b9cf6516cdd8647dae7f2a2e4106618e543f6c0106a734/Somalia-IPC-Time-Series.jpeg" /> <img width="2800" height="1400" width_o="2800" height_o="1400" data-src="https://freight.cargo.site/t/original/i/9005d39f52b0f64245d0c6fdab0a4e1d2beb605abb480be5c719b1811b72e12e/Nigeria-FEWSNET.jpeg" data-mid="37942810" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/9005d39f52b0f64245d0c6fdab0a4e1d2beb605abb480be5c719b1811b72e12e/Nigeria-FEWSNET.jpeg" /> <img width="2200" height="1400" width_o="2200" height_o="1400" data-src="https://freight.cargo.site/t/original/i/70da1ede00ee827db86941be4c240e93faa30393f35dd8d8401757a77359f276/Plot-History.jpeg" data-mid="37942843" border="0" src="https://freight.cargo.site/w/1000/i/70da1ede00ee827db86941be4c240e93faa30393f35dd8d8401757a77359f276/Plot-History.jpeg" /> <img width="2000" height="2000" width_o="2000" height_o="2000" data-src="https://freight.cargo.site/t/original/i/1566c4a9acad548b02e090cf4f9bd56ee58a2367265e1055de1b83e194ce76ba/Nigeria-FEWS-IPC-Time-Series.jpeg" data-mid="37461636" border="0" src="https://freight.cargo.site/w/1000/i/1566c4a9acad548b02e090cf4f9bd56ee58a2367265e1055de1b83e194ce76ba/Nigeria-FEWS-IPC-Time-Series.jpeg" /> https://andres-chamorro.com/Poverty-Environment-Linkages Sat, 16 Sep 2017 03:58:56 +0000 Andres Chamorro https://andres-chamorro.com/Poverty-Environment-Linkages What is the relationship between poverty and natural resources? Who bears the burden of environmental degradation? To answer these questions, our team curated the best publicly available global datasets, and created a sub-national database of socio-economic and environmental statistics. <img width="791" height="412" width_o="791" height_o="412" data-src="https://freight.cargo.site/t/original/i/1cefda52b49802ccfc8b70611edf32699d5f440cc56aa1a51e5f346aa744e90a/forest.png" data-mid="37869343" border="0" src="https://freight.cargo.site/w/791/i/1cefda52b49802ccfc8b70611edf32699d5f440cc56aa1a51e5f346aa744e90a/forest.png" /> <img width="836" height="836" width_o="836" height_o="836" data-src="https://freight.cargo.site/t/original/i/38f69ff38f0e21bd0d25bcda14a9cb13f4a2ebfa4afa961c7af49d6de455612b/pollution.png" data-mid="37869347" border="0" src="https://freight.cargo.site/w/836/i/38f69ff38f0e21bd0d25bcda14a9cb13f4a2ebfa4afa961c7af49d6de455612b/pollution.png" /> <img width="2000" height="1600" width_o="2000" height_o="1600" data-src="https://freight.cargo.site/t/original/i/1d6283f903075b51f6a8870832170b4315596afe89e90ec4c344f9f1d458afa3/Fig1.2.jpeg" data-mid="37869330" border="0" src="https://freight.cargo.site/w/1000/i/1d6283f903075b51f6a8870832170b4315596afe89e90ec4c344f9f1d458afa3/Fig1.2.jpeg" /> <img width="1400" height="700" width_o="1400" height_o="700" data-src="https://freight.cargo.site/t/original/i/cefcaa545c3baca4eaa0901e1b9fa6ff44b63a994f24d1b77f07c9f3e4cf591d/Fig2.3.jpeg" data-mid="37869338" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/cefcaa545c3baca4eaa0901e1b9fa6ff44b63a994f24d1b77f07c9f3e4cf591d/Fig2.3.jpeg" /> <img width="1400" height="800" width_o="1400" height_o="800" data-src="https://freight.cargo.site/t/original/i/ad55de111e8fd5471c2d4823a9096ea059b2f97361099fde43e7bd265dc34d0c/Fig2.2.jpeg" data-mid="37869333" border="0" src="https://freight.cargo.site/w/1000/i/ad55de111e8fd5471c2d4823a9096ea059b2f97361099fde43e7bd265dc34d0c/Fig2.2.jpeg" /> <img width="1200" height="700" width_o="1200" height_o="700" data-src="https://freight.cargo.site/t/original/i/33490bc68ae1c5074daac6e471f35e00c9afb81dcb8a2087d05d29544d6df28c/Fig2.7.jpeg" data-mid="37869340" border="0" src="https://freight.cargo.site/w/1000/i/33490bc68ae1c5074daac6e471f35e00c9afb81dcb8a2087d05d29544d6df28c/Fig2.7.jpeg" /> Sample WorkI built an interactive tool to visualize bivariate correlations in our dataset. After seeing the website crash multiple times when reading the global dataset, I set up an API to load country data on demand. <img width="2462" height="1286" width_o="2462" height_o="1286" data-src="https://freight.cargo.site/t/original/i/041748c38da20f020b4f1711552a2a9c44260d1d1fb44ac47fa73f17d24bd20b/Screen-Shot-2019-04-11-at-9.01.26-AM.png" data-mid="39803484" border="0" src="https://freight.cargo.site/w/1000/i/041748c38da20f020b4f1711552a2a9c44260d1d1fb44ac47fa73f17d24bd20b/Screen-Shot-2019-04-11-at-9.01.26-AM.png" /> I helped extract statistics from a number of gridded datasets, using the arcpy python module from ArcGIS.I led the production of the Africa Forest Poverty Atlas, using mapnik and D3.js to automate the production maps and charts for 20 countries, refining the final layout in Adobe Illustrator. <img width="1650" height="1275" width_o="1650" height_o="1275" data-src="https://freight.cargo.site/t/original/i/6036ac8ba9907fc0415c75672efd8913dfcb5a94a9ed1860916fbfb26a3076c8/Intro.jpg" data-mid="37866104" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/6036ac8ba9907fc0415c75672efd8913dfcb5a94a9ed1860916fbfb26a3076c8/Intro.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/655e37af31e4305a86ab65c385c4311e3776dfcd0d5d9bdef2ec8a3c9f908eaf/Ghana_LandCover-01.jpg" data-mid="37866103" border="0" src="https://freight.cargo.site/w/1000/i/655e37af31e4305a86ab65c385c4311e3776dfcd0d5d9bdef2ec8a3c9f908eaf/Ghana_LandCover-01.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/a38b01e0e02c21453d1f75666072493afb390a5aec8dd86c89fe61cf40c1f288/Ghana_2-01.jpg" data-mid="37869914" border="0" src="https://freight.cargo.site/w/1000/i/a38b01e0e02c21453d1f75666072493afb390a5aec8dd86c89fe61cf40c1f288/Ghana_2-01.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/f428901d93f61ef33b723bb600cb74609bb1864ff515cd6a4fb80c8c23c97696/Ghana_3-01.jpg" data-mid="37866098" border="0" src="https://freight.cargo.site/w/1000/i/f428901d93f61ef33b723bb600cb74609bb1864ff515cd6a4fb80c8c23c97696/Ghana_3-01.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/e643bf2e1c17ccc6dcb79340b57d5f2c7eb38ea03aa8292431f35749a6842d85/Ghana_4-01.jpg" data-mid="37866099" border="0" src="https://freight.cargo.site/w/1000/i/e643bf2e1c17ccc6dcb79340b57d5f2c7eb38ea03aa8292431f35749a6842d85/Ghana_4-01.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/c96a82318090cbe30fe7473f8521af1dd94d9e02f20d16ae26153c015d21849f/Ghana_5-01.jpg" data-mid="37866101" border="0" src="https://freight.cargo.site/w/1000/i/c96a82318090cbe30fe7473f8521af1dd94d9e02f20d16ae26153c015d21849f/Ghana_5-01.jpg" /> <img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/257e5a15991b8612ab466d272af9a69ce4a1e2a10519cf47e123c4dadd894bec/Ghana_6-01.jpg" data-mid="37866102" border="0" src="https://freight.cargo.site/w/1000/i/257e5a15991b8612ab466d272af9a69ce4a1e2a10519cf47e123c4dadd894bec/Ghana_6-01.jpg" /> The Hidden Dimensions of Poverty Database is now publicly available here. https://andres-chamorro.com/Financial-Inclusion Mon, 18 Feb 2019 19:47:46 +0000 Andres Chamorro https://andres-chamorro.com/Financial-Inclusion The Project The World Bank’s Financial and Innovation Practice was tasked with conducting a geospatial mapping of financial access points, with the goal of identifying underserved areas where ATMs and banking services should be expanded. I provided technical support on the following tasks: Geocoding access points &nbsp; &nbsp; - Using the Geonames and the Google Maps API, I geocoded each access point to create a point dataset suitable for overlaying population and demand-side statistics. Calculating travel time buffers from access points&nbsp; &nbsp; - The team was also interested in exploring financial exclusion from a distance perspective. How far do people need to drive to get to the nearest ATM?&nbsp; &nbsp; &nbsp; - The implementation of the analysis uses a great python package developed by our team to conduct network analysis at scale. The package helps transform Open Street Map data into a graph network object, leveraging the open-source libraries peartree and osmnx. https://andres-chamorro.com/DC-Interactive-Maps Sat, 16 Sep 2017 00:50:24 +0000 Andres Chamorro https://andres-chamorro.com/DC-Interactive-Maps 1. Changes in DC neighborhoods, by the block <img width="2376" height="1238" width_o="2376" height_o="1238" data-src="https://freight.cargo.site/t/original/i/d6542f966aada036b486ddd798f1ff301991e564e053f43248f997ba09d785a7/Screen-Shot-2019-03-23-at-4.27.13-PM.png" data-mid="38345204" border="0" src="https://freight.cargo.site/w/1000/i/d6542f966aada036b486ddd798f1ff301991e564e053f43248f997ba09d785a7/Screen-Shot-2019-03-23-at-4.27.13-PM.png" /> 2. An animation of Bikeshare trips<img width="2518" height="1362" width_o="2518" height_o="1362" data-src="https://freight.cargo.site/t/original/i/a0e01202da73ee9f56eedb719b4087f553326fec149b38ef2bbab873df80436e/Bikeshare_screenshot.png" data-mid="3918862" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/a0e01202da73ee9f56eedb719b4087f553326fec149b38ef2bbab873df80436e/Bikeshare_screenshot.png" /> https://andres-chamorro.com/Forest-Fires Sat, 16 Sep 2017 01:09:47 +0000 Andres Chamorro https://andres-chamorro.com/Forest-Fires Heatmap web service of fire history in Indonesia Service can be accessed&nbsp;here. <img width="2522" height="1318" width_o="2522" height_o="1318" data-src="https://freight.cargo.site/t/original/i/1402bcfd0d82ef12c19c378827226cb518c57db655755fb4820d4c5c21b6a7f1/Screen-Shot-2019-03-17-at-10.55.14-PM.png" data-mid="37872440" border="0" src="https://freight.cargo.site/w/1000/i/1402bcfd0d82ef12c19c378827226cb518c57db655755fb4820d4c5c21b6a7f1/Screen-Shot-2019-03-17-at-10.55.14-PM.png" /> Interactive dashboard of fire counts by region and land type&nbsp;<img width="1658" height="1222" width_o="1658" height_o="1222" data-src="https://freight.cargo.site/t/original/i/7385df183402b603e7f070047cd3727f46b98ae6a975a077a6635ed4e112dc38/Screen-Shot-2019-03-18-at-4.50.03-PM.png" data-mid="37941146" border="0" src="https://freight.cargo.site/w/1000/i/7385df183402b603e7f070047cd3727f46b98ae6a975a077a6635ed4e112dc38/Screen-Shot-2019-03-18-at-4.50.03-PM.png" /> https://andres-chamorro.com/Elecciones-Segun-Twitter Sat, 16 Sep 2017 00:51:11 +0000 Andres Chamorro https://andres-chamorro.com/Elecciones-Segun-Twitter A scrollytelling exploration of tweets related to the 2016 Nicaraguan presidential elections. <img width="2382" height="1228" width_o="2382" height_o="1228" data-src="https://freight.cargo.site/t/original/i/23473f04311f032dfc4aefc9797dce10ac97090e79a36b8bd18a8f3a8c229e6e/Screen-Shot-2019-02-17-at-6.50.00-PM.png" data-mid="35645257" border="0" src="https://freight.cargo.site/w/1000/i/23473f04311f032dfc4aefc9797dce10ac97090e79a36b8bd18a8f3a8c229e6e/Screen-Shot-2019-02-17-at-6.50.00-PM.png" /> https://andres-chamorro.com/Apagon-Venezuela Sun, 14 Apr 2019 17:50:03 +0000 Andres Chamorro https://andres-chamorro.com/Apagon-Venezuela This map series explores the recent power outage in Venezuela, as captured by NASA’s VIIRS Nighttime Lights imagery. <img width="1495" height="1318" width_o="1495" height_o="1318" data-src="https://freight.cargo.site/t/original/i/d8b1d15012f000ac977138ac52bf1dbd25483dacc098b9ba5fd0534013d8fe7e/Luces-Mar-6.jpg" data-mid="40011113" border="0" src="https://freight.cargo.site/w/1000/i/d8b1d15012f000ac977138ac52bf1dbd25483dacc098b9ba5fd0534013d8fe7e/Luces-Mar-6.jpg" /> <img width="1495" height="1318" width_o="1495" height_o="1318" data-src="https://freight.cargo.site/t/original/i/aa6b104c054085733cd8d04a90b7945e0be2e86240ac5cbffadec5b678b23c76/Luces-Mar-7.jpg" data-mid="40011114" border="0" src="https://freight.cargo.site/w/1000/i/aa6b104c054085733cd8d04a90b7945e0be2e86240ac5cbffadec5b678b23c76/Luces-Mar-7.jpg" /> <img width="1495" height="1318" width_o="1495" height_o="1318" data-src="https://freight.cargo.site/t/original/i/1abb0d756472b2e68dae4d24df2bed547d01473bec46567e737be3aa37ab61f8/Luces-Mar-8.jpg" data-mid="40011115" border="0" src="https://freight.cargo.site/w/1000/i/1abb0d756472b2e68dae4d24df2bed547d01473bec46567e737be3aa37ab61f8/Luces-Mar-8.jpg" /> <img width="1495" height="1318" width_o="1495" height_o="1318" data-src="https://freight.cargo.site/t/original/i/579d7ea35a3555bf141b07dd3181951a52e5a24d577901fdc954a4d050353996/Luces-Mar-9.jpg" data-mid="40011116" border="0" src="https://freight.cargo.site/w/1000/i/579d7ea35a3555bf141b07dd3181951a52e5a24d577901fdc954a4d050353996/Luces-Mar-9.jpg" />