Data Lab Strategic Brief: Türkiye Earthquake Impact Monitoring

A Strategic Brief is a high-level set of recommendations prepared by the Data Lab that address a given challenge. Recommendations may include internal and external data resources, specific colleagues and/or teams with relevant expertise, data management best practices, suggestions for exploration, and/or a list of relevant resources and similar projects.

Should you have any questions about the Brief, please contact: datalab@worldbank.org

Project Overview

On February 6, 2023, a powerful 7.8 magnitude earthquake and a series of strong tremors and aftershocks wrought substantial damages across southeastern Türkiye and northwest Syria. As of time of writing, the death toll has passed 40,000 and the earthquake’s aftermath is substantially impacting the people, infrastructure, and economies of the two countries.

The World Bank announced $1.78 Billion for Türkiye’s recovery and reconstruction efforts.Effective World Bank and donor interventions will require a deep, data-driven understanding of these impacts.

The Türkiye Country Management Unit has requested advisory on data and analytical resources that may support measurement and monitoring of socio-economic impacts, including population displacement and business impacts.

The Data Lab advisory is presented in three sections:

• Data Collection and Acquisition: Identified data resources that could support the earthquake socio-economic impact analysis.

• Data Management: Recommendations for managing derived project datasets.

• Data Analytics and Insight Dissemination: A menu of proposals for analytical work that could be coordinated through the Lab.

Data Collection and Acquisition

This section includes a range of data collection and acquisition recommendations, such as open data resources, leveraging private data partnerships, current World Bank subscriptions and licenses, survey solutions, and remote sensing.

Official UN Earthquake and Refugee Impact Reporting

ID	Dataset	Description	License	Access
1	UN OCHA Humanitarian Data Exchange Türkiye-Syria Earthquakes Collection	Collection covering damage; deaths and injuries; location of points of interest, including financial service centers, health facilities, and schools.	Open	Türkiye-Syria Earthquakes Collection
2	UNHCR Türkiye Country Focus	Regular text and pdf updates on refugee activities, particularly refugees entering Türkiye from Syria	Open	UNHCR Türkiye Country Focus

Population,Demographics, and Human Settlement Data

ID	Dataset	Description	License	Access
3	Facebook Population During Crisis	The number of Facebook users observed in a location following a crisis compared to a precrisis baseline period. Although this dataset is limited to Facebook users, it is the most frequently updated estimate of population movement available.	Proprietary	Data can be accessed by submitting a proposal to the Development Data Partnership.
4	Meta High Resolution Population Density Maps	Meta, in collaboration with Center for International Earth Science Information Network (CIESIN), used artificial intelligence to identify buildings from satellite imagery and, with census and other data, derived population estimates at a 30-meter resolution. The 2020 dataset includes a spatial breakdown of population by gender and age.	Open	HdX; The Data Lab has extracted this data and made available on WB SharePoint
5	WorldPop Population Density	WorldPop released the population dataset for Türkiye from 2020 at a 100m resolution. This dataset is available in a .tif format.	Open	WorldPop Population Dataset and Metadata
6	Türkiye Official Population, Migration, and Income Statistics	The Turkish Statistical Insitute hosts data for population disaggregated by gender, age, municipality as early as 2023. They also have migration statistics published in December 2022. Statistics also include information on regional relative poverty, disposable income of households, and other income distribution statistics.	Open	Türkiye Official Statistics for Population and Migration; for Income
7	Atlas AI Human Settlement Geospatial Layer	Atlas AI released their Atlas of Human Settlements data for Turki and Syria from 2021 for humanitarian needs. The data show the extent of human settlements and population prior to the earthquake and include three data products – a Built-up Surface Map, estimating human presence in each 100 sq-m unit area; a Built-up Index Map, a scaled estimate of the extent of human presence in each 100 sq-m unit area, inferred from buildings; and a Built-up Settlement Map which is a vector representation of the built-up surface, spatially aggregated within a 50m radius.	Open (for Türkiye and Syria only; else proprietary)	Human Settlement Layer. For latest information from the same company, contact the Development Data Partnership.
8	Meta Relative Wealth Index	The Relative Wealth Index from Meta identifies areas that are richer/poorer in comparison to other areas within the country. This dataset has been made publicly available for Türkiye and can be used to identify areas where there are likely to be more vulnerable populations.	Open (for Türkiye only; else proprietary)	Relative Wealth Index

Satellite Imagery for Detecting Land Surface Changes

ID	Dataset	Description	License	Access
9	United States Geological Survey Landsat	Satellite imagery data that can be used to track changes in land area.	Open	Landsat
10	Copernicus Sentinel Data	Copernicus Open Access Hub hosts Sentinel radar data that can be used to track land area changes, regardless of cloud cover.	Open	Sentinel

Geospatial Infrastructure Data (Roads, Buildings, Power Grids, Internet Connectivity)

ID	Dataset	Description	License	Access
11	Open Street Maps	Open Street Map hosts crowd-sourced points of interest, amenities, roads, and other physical features for Türkiye and neighboring countries. There is an active community of volunteers updating these maps, including labeling collapsed buildings.	Open	OpenStreetMaps; Query for pulling the latest collapsed building data
12	Microsoft Building Footprints	Microsoft has used AI to generate recent baseline building footprints for Türkiye and northern Syria (dated November 2022) that are accessible through an open-source license.	Open	Building footprint respository; Footprints for Türkiye and Syria:  WB SharePoint
13	Meta Electricity Grid Distribution Maps	Using gridfinder, an open-source tool for predicting the location of electricity network lines using night-time lights satellite imagery and OpenStreetMap data, Meta has released a map that shows the predicted electricity distribution grid and transmission lines.	Open	Electricity Distribution Maps
14	Ookla Speedtest Intelligence Data	Ookla provides internet connectivity data which allows to see pre-earthquake and post-earthquake internet speeds across the country. This can be used as a proxy to identify areas that would be harder to reach and a proxy to identify areas where infrastructure may be damaged because of the earthquake. Quarterly data at a subnational level is available publicly and the more temporally and spatially granular datasets can be accessed through The Partnership.	Open and Proprietary	Quarterly data: Ookla Open Data portal Daily data: accessible by submitting a proposal to the Development Data Partnership.
15	Network Coverage Maps by Meta	Network Coverage Maps show where people using Facebook have cellular connectivity. This data provides information about gaps in cellular network coverage after natural disasters for the purpose of restoring connectivity as well as determining where aid workers and communities will be able to communicate. This dataset is available on a daily basis.	Proprietary	Submit a proposal to the Development Data Partnership

Data for Understanding Critical Needs and Access to Services

ID	Dataset	Description	License	Access
16	GDLET Project (News data)	The GDELT Project “monitors the world’s broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events.”  While there are some limitations to the use of the GDELT APIs, if the team were interested in tracking news mentions of specific key words or sentiments by time and place, this is a useful starting point, since the data are free and open source.	Open	GDELT Project; For support running queries: datalab@worldbank.org
17	Premise Humanitarian Needs Surveys	Premise has had humanitarian data collection up and running in Türkiye since the earthquake. Some of their data include, surveys of displaced people and observational data related to medical supply, road conditions etc. They can also conduct custom surveys based on the needs of World Bank teams.	Open for Türkiye until 3/15/23; Proprietary otherwise	Premise survey data availability list. To access, contact datapartnership@worldbank.org
18	Google Search Trends	Search trends data from the Google Trends API can be used to identify trends in search terms of specific words over time. For instance, a change in search terms related to ‘medicine’ or ‘food’ can indicate a specific need in different parts of the country.	Proprietary	Submit a proposal to the Development Data Partnership.

Data for Measuring Economic Activity (Direct and Proxies)

ID	Dataset	Description	License	Access
19	Nighttime Lights	Nighttime lights have proven to be useful predictors of numerous dimensions of human activity: electrification, population, GDP, etc. Recent developments have made the entire nightly archive of nighttime lights available in the public domain, and novel tools have made generating a consistent timeline of nighttime lights across the DMSP and VIIRS sensors possible. These data are accessible through a WB collaboration with the US National Oceanic and Atmospheric Administration (NOAA).	Open	NightTime Lights Data. For support in utilizing Nighttime Lights, contact the Geospatial Operations Support Team (GOST): gost@worldbank.org or Rob Marty (DIME).
20	Meta Business Activity Trends	Business Activity Trends measures relative Facebook business page activity (posts, comments, etc.). Index values are compared to a rolling three month-prior baseline. The data is released daily and at a subnational level, aggregated by sector (e.g., retail, grocery). During crises, the expectation is that activity levels will decline.	Proprietary	Submit a proposal to the Development Data Partnership
21	Türkiye Official Economic Statistics	The Turkish Statistical Institute has released Producer Price Index (PPI) for agricultural products, domestic PPI and other inflation related indicators as early as 2023. They also have data on Economic Confidence Index.	Open	Turkish Statistical Institute Economic Indicators

Data for Monitoring the Movement of People

All the datasetswithin this category can be accessed by submitting a proposal to the Development Data Partnership.

ID	Dataset	Description	License	Access
22	Meta Movement between Places during Crises	Movement Between Places during Crisis shows how many Facebook users moved from one area to another and if this movement is more or less than a normal day before a crisis or event.	Proprietary	Submit a proposal to the Development Data Partnership
23	Outlogic Observation Panel (Mobile Device GPS Data)	Outlogic collects a mobile location data panel that includes mobility metrics (speed, bearing, altitude, vertical accuracy) and other detection capabilities (IoT, Wi-Fi, and Beacon). Türkiye has a significant number of devices whose GPS data is recorded through the panel that can be used to monitor population movement. The Data Lab team has used these data to track movement across borders between Syria and Lebanon.	Proprietary	Submit a proposal to the Development Data Partnership
24	Veraset Movement Data	Veraset provides a similar service to Outlogic – mobility data comprises population location and movement data derived primarily from mobile device GPS, Wi-Fi, and IoT signals. A similar analysis to Outlogic can be performed using Veraset da	Proprietary	Submit a proposal to the Development Data Partnership
25	Mapbox Movement	Mapbox Movement is a global data set derived from 20B+ location updates daily, which may be used to understand aggregate activity, density, and movement over time at the city, regional, or country scale.	Proprietary	Submit a proposal to the Development Data Partnership
26	Mapbox Traffic Matrix API	The Mapbox Matrix API is built on anonymized mobile device telemetry data and supports large scale traffic and road network analyses. The API may be used to identify areas poorly served by critical services. This dataset has been used in the past by WB colleagues to analyze spatial accessibility of health facilities. The usefulness of this dataset depends on the availability of Mapbox data for Türkiye.	Proprietary	Submit a proposal to the Development Data Partnership

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Data Management

This section includes data privacy policy, data storage and access policy and infrastructure, compute infrastructure, data license compliance, data security classifications, data sovereignty policy, etc.

Privacy and Security Considerations

At this stage, the Lab does not have specific recommendations to the team regarding data management. That said, the Lab would like to ensure the team is aware that some analyses conducted over the course of the earthquake relief project may involve use of sensitive data and/or generate sensitive results. The Bank has two sets of guiding policies for secure data management and for protecting the privacy of individuals. The Task Team Leader and team members working closely with sensitive data and/or results are advised to review these policies before beginning project implementation.

• World Bank Policy on Data Privacy

• World Bank Policy on Access to Information

Data Storage

As data are sourced and analyzed, the Lab recommends they is added to the World Bank Development Data Hub. This will ensure data are available to all Bank staff (as appropriate) for reuse, minimizing additional effort and duplication of acquisition by other teams. For assistance making data available, contact Rochelle O’Hagan (rohagan@worldbank.org).

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Data Analytics and Insight Dissemination

With acquired data and sufficient data management procedures and infrastructure in place, how do we responsibly generate and share insights from these data? This section includes economics and statistical analysis, data science (AI/ML), app development, geospatial analytics, code collaboration best practices, reproducible code best practices, data product licensing, etc.

The Türkiye Country Economist has specifically requested recommendations for measuring and monitoring population displacement and business impacts.

For each activity, the Data Lab can generate reusable methods and initial insights, and then train Bank teams to continue adjusting the methodology and generating the insights themselves, as needed. To the extent possible, insights will be geospatially aggregated to a granular level, enabling the CMU to quickly visualize correlations between indicators by location – e.g., relative damage index, population demographics, population movement, nighttime lights, internet availability, etc.

The Lab coordinates the production of analytical work with team members from across the Bank, including (but not limited to) colleagues from DEC Analytics and Tools, Global Operations Support Team, GFDRR, Development Data Hub, Development Data Partnership, ITS Technology and Innovation Lab, and ITS.

I.      Understanding Displacement Patterns

The Data Lab can undertake the following activities to better understand population movements (who, where, and when) resulting from the earthquake:

Topic	Description
Data Sources	All movement datasets (#22-26) | All population data layers (#3-7) | UNHCR and UNOCHA migration data (#1,2) | and Open Street Map Points of Interest (#11)
General Approach	With the popularization of smartphone usage and connectivity across the world, the last decade has witnessed the emergence of massive mobility datasets, such call detail records, geo-tagged posts from social media platforms and generated from GPS devices. These datasets have propelled a rich scientific production on various applications of mobility analysis, ranging from epidemiology to disaster resilience, urban planning and transportation engineering. Through the Development Data Partnership,staff now have access to high-frequency location-based mobility data, constituted of high temporal and spatial resolution timestamped geographical points generated by GPS-enabled devices. By overlaying population and socioeconomic data with mobility data, the project team will be able to produce an analysis based on the estimation of home locations and population displacement to be delivered on a weekly basis. The results could shed light on the earthquake’s effects on different areas and/or populations aggregated by seismic intensity, wealth group or additional features that become available. In addition, a point of  interest rate visit analysis may show to what degree the decrease in movement and activity in business and commercial centers may correlate to the economic outlook, comparing to historical data.The project team may leverage both Meta and mobile device generated data, as well as official UN statsitics and other data sources as they become available to support cross-validation. All analytics will be supported by at least two data sources, where possible – for example, combining Meta movement statistics with mobile-device location data and comparing with official UNHCR data. See also: Mobility for resilience: displacement analysis — mobilkit documentation and Observed Cross-Border Mobility Traces in Lebanon and Syria
Outputs	Data Pipeline and Pre-Processing. The Development Data Partnership handles data delivery, data management and data engineering on behalf of all  staff and, in doing so, creates economies of scale. Transfer and storage costs are absorbed by the Lab. Datasets. All datasets used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Displacement by seismic intensity, home locality, wealth group. Maps.  Displacement by seismic intensity, home locality, wealth group. Training and Support. The Lab will provide the CMU with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed.
Limitations	The methodology relies on private intent data in the form of mobile location data. In other words, the input data was not produced or collected to analyze the population of interest or address the research question as its primary goal but repurposed for the public good. The< benefits and caveats when using private intent data have been extensively discussed in the World Development Report 2021.                                                                    On the one hand, the mobility data panel is spatially and temporally granular and readily available, on the other hand it is created as a convenience sampling which constitutes an important source of bias. The panel composition is not entirely known and susceptible to change, the data collection and the composition of the mobility data panel cannot be controlled. For example, Outlogic has reported a significant data reduction since April 30, 2022, which impacted the panel composition.                                                            Due to the nature of the problem and the cardinality, the results can be complex and better analyzed and interpreted when looking at the interactive maps, instead of annual indicators.
Estimated Resources	Compute and Storage. AWS S3 and AWS EC2 r6i.16xlarge (under Data Lab’s custody). ITS charges for mobility data compute and storage services. Staff Hours. ~36 hours GF-level staff time.

II. Understanding Economic Impacts

The Lab proposes focusing on the following key business activity and business supporting infrastructure indicators:

II.A. Observed Electricity Usage at Night

By comparing pre- and post-earthquake nighttime light data, we can identify changes in availability of electricity, mass movements of people, and changes in oil refining (through flaring). Some of our sample work using this methodology can be seen as part of the Syria Economic Monitor.

Topic	Description
Data Sources	Nightly VIIRS nighttime lights (#19) Global Gas Flaring Reduction Partnership for locations of gas flaring facilities.
General Approach	1.     Download, clean, pre-process data for area of interest. 2.     Generate separate< map layers for lights observed in gas flaring locations and lights in other locations. 3.     Generate maps and statistics showing changes in nighttime light intensity over time y area of interest.  Here is an example.
Outputs	Datasets. All datasets< used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Bi-weekly percent change in nighttime light intensity by area of interest. Maps. Static maps showing percent change in observable nighttime lights (with and without flaring) over time, aggregated by a common geospatial index for cross-indicator comparison, and/or aggregated by area of interest. Training and Support. The Lab will provide the CMU team with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed
Limitations	Nighttime lights are a common data source for measuring local economic activity. However, it is a proxy that is strongly—although imperfectly—correlated with measures of interest, such as population, local GDP, and wealth. Consequently, care must be taken in interpreting reasons for changes in lights.
Estimated Resources	Compute. Nighttime light analysis compute costs scale with the area of interest. For the earthquake zone, these costs would be negligible and not charged to the project team. Staff Hours. 24 hours GF-level staff time

II.B. Internet Connectivity Availability and Quality

In Türkiye, the internet underpins modernbusiness and the government’s ability to communicate resources to those in need. Understanding changes in availability post-earthquake are an immediate proxy for damages to this critical infrastructure.

Topic	Description
Data Sources	Ookla Speedtest Intelligence Data (#14) Meta Network Coverage Maps (#15) Geospatial layer of earthquake-impacted locations (#1,2, CMU)
General Approach	1.     Obtain Ookla  baseline and most recent subnational data for internet quality through the  Development Data Partnership. 2.     Compare data from Ookla and Meta’s network connectivity maps 3.     Identify areas where there was an earthquake and verify if there is a change in internet connectivity pre-event and post-event.
Outputs	Datasets. All datasets used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Change in internet speed/network connectivity at a province/district level | Identified districts where there might be people that are difficult to reach via the internet  Maps. Combined network connectivity map using both Ookla and Meta datasets.  Training and Support. The Lab will provide the CMU team with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed.
Limitations	Facebook Network Coverage Maps are only pertaining to Facebook users. In Türkiye, as of October 2022, there are more than 60 million active Facebook users. The population of the country is roughly 84.78 million. Although this could make Facebook data fairly reliable in Türkiye, it does not cover everyone. Ookla speedtest data is a collection of speed tests conducted on the Ookla platform. This dataset also does not cover the entire population. Additionally, their dataset is crowdsourced, and their sampling methodology is untested.
Estimated Resources	Staff Hours. 20 hours GF-level staff time

II.C. Observed Business Facebook Page Activity

Many local businesses use Facebook as their primary business website. Facebook records levels of activity on these pages, aggregates activity by location and sector, and presents as generalized trend indicators for levels of business activity.

Topic	Description
Data Sources	Business Activity Trends from Meta (#20) | Geospatial layer of earthquake locations (#1,2, or from CMU) | Network Connectivity Maps from Meta (#15)
General Approach	1.     Identify areas where there is a change in Business activity compared to a baseline in November 2022. 2.     Identify if the change can be attributed to the earthquake or just change in network connectivity by overlaying with geospatial earthquake affected area map and network connectivity map.
Outputs	Datasets. All datasets used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Sectors that have been potentially impacted due to the earthquake | Districts/provinces in which business activity has the the most amount of change Maps. Business activity trend map corrected for change in network coverage due to the earthquake. Training and Support. The Lab will provide the CMU and DNA teams with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed.
Limitations	Business Activity Trends captures the rate of posting (posts and visits) on Facebook business pages. Thus, offline and off-Facebook activities are not captured and along with social media usage, connectivity and cultural customs, may contribute as serious limitation and source of bias. In other words. the index is not representative of the other business happening in the area. There is also a bias towards the service sector as they are more likely to be active on Facebook.
Estimated Resources	Staff Hours. 16 hours GF-level staff time

II.D. Damaged or Inaccessible Local Businesses

By layering road and building footprint damages with points of interest, we can generate statistics on impacted businesses, banks, and other ancillary services critical to local functioning economies.

Topic	Description
Data Sources	Earthquake damaged buildings geospatial layer (#1,2, and/or CMU) | Road network and point-of-Interest data (from government, or, if not available, from Open Street Map, supplemented by Premise survey data for field verification (requires a separate contract with Premise) (#11, 17)
General Approach	1.     Collect point-of-interest data. If not readily available, coordinate with Premise under the WB’s current contract to verify existing Open Street Map PoI data by conducting surveys of residents to determine which businesses have been lost across the 5,000+ destroyed buildings and/or those that are no longer accessible due to road damages. 2.     Set up baseline map layer: Add roads and admin boundaries and intersect building footprints with business points-of-interest. 3.     Overlay baseline map with damaged road and building footprint layer. 4.     Generate a list, by sector and local admin boundary, of directly damaged businesses. 5.     Run a network analysis to identify those businesses that are not accessible because of roadway damage.
Outputs	Datasets. All datasets used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Number of damaged/inaccessible businesses by sector and by local admin boundary. Map. Single map showing locations of damaged or inaccessible businesses. Training and Support. The Lab will provide the CMU and DNA teams with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be  available for technical assistance, as needed.
Limitations	Analysis is contingent on availability of good point-of-interest data.
Estimated Resources	Staff Hours. ~ 40 GF-level staff hours (more if field verification is used)

II.E. Changes in Financial Transaction Activity by Sector

To understand better the timing of cessation and resumption of retail business activity, we can try to monitor changes in levels of transaction data (for credit card payments).

Topic	Description
Data Sources	Financial transaction volume data by town and by sector – the Lab is currently in negotiations with Visa and Mastercard for signing a pro-bono data sharing agreement and can try to expedite progress by making a direct request related to earthquake response.
General Approach	1.     Since data would be provided by a third-party, conduct basic data checks and generate descriptive statistics. 2.     Prepare simple map and data visualizations showing changing trends in financial transaction activity by sector.
Outputs	Datasets. All datasets used in the analysis will be documented and stored on a project SharePoint (for the CMU’s ease of use), with strictly managed permissions (per terms of the license agreement with the financial transaction firms). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. % Change in financial transactions by sector and by neighborhood, pre and post-earthquake, aggregated by week. ·       Map. Dynamic web map that shows percent change in weekly aggregated financial transactions by sector and neighborhood, over time. Training and Support. The Lab will provide the CMU and DNA teams with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed.
Limitations	Analysis is contingent on successful negotiations with financial transaction firms. Transaction data would not include cash-based or local mobile-payment transactions and would only reflect transactions made with the company providing the data.
Estimated Resources	Staff Hours. ~24 GF-level staff hours

III. Understanding Social Impacts

To get a sense of “need intensity,” the team can explore use of the Google Health Trends API (available through the Development Data Partnership) to determine temporally and by region changes in queries related to key terms provided by the Task Team (e.g., ““water,” “money transfer,” “pediatrician,” “hospital,” “Covid”). The method can be adjusted to incorporate other datasets, such as social media data (e.g., Twitter which has roughly 16 million users in Türkiye).

The Lab can also explore and analyze Premise survey data (available through the Development Data Partnership),which includes changes in availability of key services, accessibility of healthcare, needs of displaced persons, and price monitoring.

Topic	Description
Data Sources	Google Search Trends API (#18) | Premise crowd-sourced survey data (#17) | Network Connectivity map obtained through a combination of Ookla + Meta internet quality (#14,15)
General Approach	1.     Identify search terms that can be queried on Google Trends API 2.     Map the changes in search terms over the weeks prior to the earthquake and weeks post the earthquake 3.     Verify if this change can be attributed to a change in network coverage 4.     Identify areas where there is an unnatural spike in number of times a search term appears 5.     Verify findings with official statistics and Premise field survey results  6.     Identify areas where there is a need based on the Premise survey and verify if such a need is absent from Google Trends because of a lack of network connectivity.
Outputs	Datasets. All datasets used in the analysis will be documented and hosted on the World Bank Development Data Hub (for ease of sharing) and a project SharePoint (for the CMU’s ease of use). Code and Documentation. All methods will be fully documented and reproducible, so that the analysis can be quickly updated by the CMU as new data are available. Indicators. Changes in surveyed prices of key goods (fuel, foods, etc.) by district; Summary of observed needs through surveys and social media by district. Maps. Dynamic map of price changes in surveyed goods by location in Türkiye over time | Dynamic map of observed needs over time by location. Training and Support. The Lab will provide the CMU and DNA teams with training so they may tweak the methodology and continue updating the insights on their own. The Lab would be available for technical assistance, as needed.
Limitations	The data obtained through Premise is crowdsourced making it a methodology that does not produce representative data samples Google Trends relies on people with an active internet connection to search, which excludes people without internet access or who do not use Google search.
Estimated Resources	Staff Hours. ~ 36 GF-level staff hours (more if field verification is used)

IV. Dissemination and Capacity Building

Since analytical results from this work could support additional teams and counterparts, the team create a centralized repository of all datasets used in analytical work an ensure all data are suitably documented and made accessible (where licensing permits), and that all methodologies are similarly made available through GitHub, so that others can reproduce the results. The Support for the Syria Economic Monitor is an example.

Additionally, the Lab can produce a web-based map for layering indicators for ready comparative analysis, as well as an Excel workbook for ease of indicator dissemination across the widest possible audience.

Additional Resources

Potential Partners and Collaborators

• The World Bank participates in a collaboration with the UN High Commissioner for Refugees (UNHCR) Joint Data Center (JDC) (https://www.jointdatacenter.org/), which supports data collection on refugees. The team may consider reaching out to the World Bank focal point for this collaboration, Harriet Kasidi Mugera (hmugera@worldbank.org), to determine what data may be available on refugees that would be relevant.

• The UN Pulse Lab Jakarta has previously developed methodologies for monitoring economic shock impacts using banking data, internal population movement using mobile phone data, and disaster response management using social media data. These methodologies may also be applicable to earthquake response. If the team were interested in collaboration, enquiries could be made to Pulse Lab Jakarta (plj@un.or.id; please cc: datalab@worldbank.org)

• Türkiye/Syria Emergency Data Science Cell: There is an inter-agency Data Science Cell set up to provide data support to Türkiye/Syria needs assessment teams. This team is moderated by UN Global Pulse through their Global Data Access Initiative. From the World Bank, colleagues from the Poverty and Equity team are a part of the pillar which measures socio-economic impact of the Earthquake. For more details, please contact Jeffrey Tanner (jtanner@worldbank.org)

Development Data Partnership’s Community and Documentation

The Development Data Partnership fosters a community of data practitioners and maintains a robust data documentation and code collaboration platform based on GitHub, which is recognized as a good practice in the World Bank and in partner international organizations. See more at https://docs.datapartnership.org.

UN Charter Activated Satellite Imagery

Following are additional geospatal datasets that may be of use to the CMU team: Earthquake related data sources.docx