ETHNO

https://docs.google.com/presentation/d/1mVy_wd8ZCMZAaNFqhYgSnXmrDJyiXwiQ/

https://opendata.org.ph/ethno/

The Philippines is one of the fastest urbanized countries in East Asia and the Pacific Region.

Highly fragmented institutional structures for urban development and metropolitan government, as well as substantial inadequacies in land administration and management, are constraining restrictions. These challenges, along with growing urbanization, have impeded city competitiveness, affecting economic growth, job creation, poverty alleviation, and livability. 

​​​​​​“Urbanization has driven regional productivity growth, but opportunities in cities have not been available to all residents and are further limited by the COVID-19 pandemic,”​.Congestion in metropolitan areas is mainly caused by poor planning and inadequate budget allocation (ADB, 2012). 

In 2019, Manila was declared by the Asian Development Bank (ADB) and TomTom Traffic Index as the most congested city that triggered a domino to the economic costs (Rapper, 2020). 

Apart from transportation, poverty and housing is also a major issue in urban cities. In 2019, it was recorded that 18.4 million people are considered urban informal settlers, apart from professional squatters, while low-income households are often forced to occupy exposed areas with low land values and vulnerable to disasters (Dizon, 2019).

Drone Use for Urban Planning in the Philippines

Many global studies have shown and applied the potential of how satellites and drones can be used for sustainable and resilient urban development and planning (Noor & Rosni, 2017.).

Even with the lack of availability of drone maps in the country, various studies have used drones and gathered drone surveys themselves in creating analysis to explore its potential in effective analysis and decision making for urban planning and development. (Ezequiel et al., 2014; Boongaling et al., 2021)

The insufficient open drone and satellite data that public and private agencies can utilize leads to unmanaged urban development and sustainability. Lack of timely and quality satellite and drone data could cause inefficient and cost-ineffective urban planning and development that may risk the ripple effects of dangerous and avoidable incidents.

Ethnography on Urban Planning and Sustainability 

Ethnography entails a commitment to trying to understand the world from another's perspective through sustained engagement in their social environments and participation in the practices that render those environments meaningful. (Reeves, 2011) 

An urban area is the region surrounding a city. Employment is mostly centered around non-agricultural jobs. Urban areas are densely populated not only in terms of infrastructure but also its inhabitants. Where residents are culturally diverse and have juxtaposed socioeconomic differences within close proximity. (Venegas, 2010) 

Incorporating ethnography in urban planning has been used to bridge those gaps, resulting in sustainable and inclusive decision making. As seen in many countries like Brazil(Koster, 2020), Kyrgyzstan(Reeves,2011), and India (Croshere, 2017) .

Anchored on the framework of Sustainable Development Goals (SDGs), specifically SDG 11, we designed the ETHNO platform to focus and address targets focusing on the following themes and elements of sustainable cities and communities:

• Measure poverty incidence using data from Philippine Statistics Authority
• Visualize existing road networks and communities to diagnose the health of transport systems
• Measure air quality of cities using NO2 and PM 2.5 as parameters 
• Open public spaces will be estimated using NDVI and NDBI as satellite indicators for greenness and built-up areas, respectively.
• Using data on building footprint and street networks, determine land usage to know which areas can be used for future urban developments

Solution

We propose drone-based digital ethnography services that integrate various data sources including earth observations (EO), social media interests, road networks, socio-demographic data, and drone images for high-precision city and citizen profiling to promote sustainable urbanization, informed decision-making, and continuous community participation.

Specifically, we intend to provide the following services:

• Incentivization programs for drone enthusiasts for enrichment of aerial imagery for urban planning.
• Big data processing that highlights social interests in the area and measure opportunities or gaps within the community

How it works

ETHNO community platform receives high-quality drone images from UAV communities and enriches this with data from remote sensing and social listening to create a database of SDG indicators. For a small fee, consumers and researchers can send location queries to ETHNO to generate an urban profile and ethnographic report. UAV operators will then receive incentives based on their submitted images encouraging more drone image submissions. 

The higher the community participation, the greater the utility of the application. The system benefits from the participation of three stakeholders:

• Community - Programs that reward data collection promote sustainable practices and enhance community participation.
• Private - Enterprises can gather more information about the location for potential business opportunities.
• Public - Information about the community and its environment can help the local decision makers create more impactful policy decisions to further the sustainable urbanization initiatives. 

ETHNO will collect, analyze and present information to provide a more precise fingerprint and urban profile of cities. The platform relies primarily on the following data sources:

• Open Spaces and Land Use

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) from Sentinel 2A - are spectral indices to gather data on green vegetation and land use of cities. The values were extracted using Sentinel Hub Statistical Info Service (FIS).Population data from SEDAC, NASA - population count and population density to estimate human 

• Street Networks, city street networks, spatial indicators and building footprints from OpenStreetMap using the Osmnx Python library. We used OpenCV to estimate the land use by converting building footprints into metrics.
• We gathered different network metrics including centrality, betweenness, and modularity. Communities have also emerged from the use of Lauvian Community detection.
• Air Pollution, NO2 from Sentinel 5P via the Sentinel Hub Statistical Info Service (FIS) and PM 2.5 concentrations from the EMB-DENR to measure air quality of cities 
• UAV images of urban areas, openly-licensed aerial images from OpenAerialMap were used for analysis.

Urban Profiling through Social Interests and Mobility:

• Social Media Reach - Facebook ad targeted options by demographic, interest, behavior, connection 
• Mobility - Movement behaviour of city population from Facebook Movement Range
• Statistical / Secondary Data - Poverty Incidence and City Performance Indicators from Philippine Statistics Authority and Cities and Municipalities Competitive Index of the Department of Trade and Industry

Check our prototype solution here:

https://drive.google.com/file/d/19FeLXt8ZeQ8VJIfA3GAWsP9sHOAv0ABO/view?usp=sharing

Impact

We aim for a sustainable urbanization through the use of novel digital technologies, community participation, and public-private partnerships through the use of quality, reliable, and accessible open data. Because without quality data, wrong decisions are made, and people could be harmed.

Our goal is to bridge the information gap between the urban planners/ policy makers and the end users and shift to non-traditional but equally reliable data sources for quick and effective data-driven decision making. 

Future Plans

To ensure the sustainability of the system, we aim to improve this prototype to include community participation and incentivization program for the drone mappers. Gather enough information to provide usable reports for public policy-makers. Improve the business model to introduce big data processing insights to the public researchers, urban planners, and private sector.

We enriched our prototype platform with satellite data on population, air quality, land use, and green vegetation spaces as urban indicators sourced from the following agencies:

Socioeconomic Data and Applications Center (SEDAC), NASA

Gridded Population of the World, Version 4 (GPWv4), Population Count and Population Density (UN-Adjusted) dataset

Sentinel-5P - Tropospheric Monitoring Instrument (TROPOMI), European Space Agency

NO2 Air Quality Data

Sentinel-2 (2A) - Multi-spectral imaging instrument (MSI), European Space Agency

Normalized Difference Vegetation Index, Normalized Difference Built-up Index

NASA’s space challenges are specific, timely, and challenging. Working with people of different backgrounds -- millennial professionals, an outgoing senior, an incoming freshman, transition from first job, gave new perspectives to reach a unified solution to a global problem that has been enriching and rewarding. 

Very different than usual problem solving since it entails virtual setup and collaborations with team members in addition to COVID-19 and lockdown restrictions challenges made the hackathon different from what we were expecting.

The team chose this challenge as the Philippines is one of the fastest urbanized countries in East Asia and Pacific Region, having the need for proper, efficient and human-centric urban development and planning while tapping the unproductive and unused growing potential for spatial and drone use and its ability to unlock significant data for urban planning in the country.

The team would like to thank Mr. Dominic Vincent Ligot for the support and mentorship from development up until the finalization, the whole ETHNO team, led by Ms. Rache and Mr. Mark for surviving the 48 hours of hackathon, and the Cirrolytix team for the support and trust they gave to the ETHNO team.

Asian Development Bank. Creating Livable Asian Cities. Edited by Bambang Susantono and Robert Guild, Asian Development Bank, 2021, www.adb.org/sites/default/files/publication/697006/creating-livable-asian-cities.pdf.

---. “Improve Quality of Life, Economic Opportunities in Cities to Build Sustainable Future — ADB.” Asian Development Bank, 28 Apr. 2021, www.adb.org/news/improve-quality-life-economic-opportunities-cities-build-sustainable-future-adb.

---. Philippines: Transport Sector Assessment, Strategy, and Road Map. 2012, www.adb.org/sites/default/files/institutional-document/33700/files/philippines-transport-assessment.pdf.

Boongaling, Cheamson Garret K., et al. “Developing a Street Level Walkability Index in the Philippines Using 3D Photogrammetry Modeling from Drone Surveys.” GeoJournal, May 2021, https://doi.org/10.1007/s10708-021-10441-2.

Croshere, Cheryl. “An Ethnography of Urban Development in Delhi: The Planning Perspective.” Escholarship.org, 2017, escholarship.org/uc/item/2v67w4zs.

Cruz, Noel, and Kimberly Ronquillo. “Mapping Urban Ethnography through Streetscape Analysis: A Case Study of Barangay Kanluran, Santa Rosa City, Laguna, Philippines.” Unitas, vol. 93, no. 364-380, 2020.

Dizon, Hazel. “Philippine Housing Takeover: How the Urban Poor Claimed Their Right to Shelter.” Radical Housing Journal, vol. 1, no. 1, Apr. 2019, p. 106, radicalhousingjournal.org/wp-content/uploads/2019/04/06_Retrospectives_Dizon_105-129-1.pdf.

Ezequiel, Carlos Alphonso F., et al. “UAV Aerial Imaging Applications for Post-Disaster Assessment, Environmental Management and Infrastructure Development.” 2014 International Conference on Unmanned Aircraft Systems (ICUAS), IEEE, 2014, p. pp. 274-283, https://doi.org/10.1109/icuas.2014.6842266.

Henderson, J. Vernon, et al. “Measuring Economic Growth from Outer Space.” American Economic Review, vol. 102, no. 2, Apr. 2012, pp. 994–1028, https://doi.org/10.1257/aer.102.2.994.

Huerta, Adrian H. “Urban Ethnography: Approaches, Perspectives and Challenges.” Academia.edu, 2019, www.academia.edu/428131/Urban_ethnography_Approaches_perspectives_and_challenges.

Koster, Martijn. “An Ethnographic Perspective on Urban Planning in Brazil: Temporality, Diversity and Critical Urban Theory.” International Journal of Urban and Regional Research, vol. 44, no. 2, May 2019, pp. 185–99, https://doi.org/10.1111/1468-2427.12765.

Lindo, Nathan. AN APPLICATION of AERIAL DRONES in ZONING and URBAN LAND USE PLANNING in CANADA a Preliminary Review of Current Policies, Restrictions and Planning Direction for Aerial Drones in Canadian Cities. 2013, digital.library.ryerson.ca/islandora/object/RULA%3A3762/datastream/OBJ/download/An_application_of_aerial_drones_in_zoning_and_urban_land_use_planning_in_Canada___a_preliminary_review_of_current_policies__restrictions_and_planning_direction_for_aerial_drones_in_Canadian_cities.pdf.

Noor, Norzailawati Mohd, and Nur Aulia Rosni. “The Evolution of UAVs Applications in Urban Planning.” ResearchGate,

ResearchGate, 2017, www.researchgate.net/profile/NorzailawatiNoor2/publication/324028974_The_evolution_of_UAVs_applications_in_urban_planning/links/5ab9c39345851515f5a0d7a2/The-evolution-of-UAVs-applications-in-urban-planning.pdf.

Rappler. “Metro Manila’s Traffic Problem Explained.” Rappler, 14 Oct. 2020, www.rappler.com/newsbreak/explainers/explanation-metro-manila-traffic-public-commute-problem.

Reeves, Madeleine. “Fixing the Border: On the Affective Life of the State in Southern Kyrgyzstan.” Environment and Planning D: Society and Space, vol. 29, no. 5, Jan. 2011, pp. 905–23, https://doi.org/10.1068/d18610.

UNDRR. “Poorly Planned Urban Development.” Preventionweb.net, UNDRR, 2013, www.preventionweb.net/understanding-disaster-risk/risk-drivers/poorly-planned-urban-development.

Villamejor-Mendoza, Maria Fe. “Competitive Cities: Implications for Better Public Service.” Policy Design and Practice, Oct. 2020, pp. 1–17, https://doi.org/10.1080/25741292.2020.1832741.

World Bank. Philippines Urbanization Review: Fostering Competitive, Sustainable and Inclusive Cities. World Bank Group, 2017, documents1.worldbank.org/curated/en/963061495807736752/pdf/114088-REVISED-PUBLIC-Philippines-Urbanization-Review-Full-Report.pdf.

Center for International Earth Science Information Network - CIESIN - Columbia University. 2013. Environmental Treaties and Resource Indicators (ENTRI) Query Service. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/entri. Accessed DAY MONTH YEAR.

#sdg11, #urban, #ethnography, #satellitedata, #drone, #uav

This project has been submitted for consideration during the Judging process.