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Can modern technologies facilitate spatial and temporal price analysis?

Marko Rissanen's picture
Also available in: Français

The International Comparison Program (ICP) team in the World Bank Development Data Group commissioned a pilot data collection study utilizing modern information and communication technologies in 15 countries―Argentina, Bangladesh, Brazil, Cambodia, Colombia, Ghana, Indonesia, Kenya, Malawi, Nigeria, Peru, Philippines, South Africa, Venezuela and Vietnam―from December 2015 to August 2016.

The main aim of the pilot was to study the feasibility of a crowdsourced price data collection approach for a variety of spatial and temporal price studies and other applications. The anticipated benefits of the approach were the openness, accessibility, level of granularity, and timeliness of the collected data and related metadata; traits rarely true for datasets typically available to policymakers and researchers.

The data was collected through a privately-operated network of paid on-the-ground contributors that had access to a smartphone and a data collection application designed for the pilot. Price collection tasks and related guidance were pushed through the application to specific geographical locations. The contributors carried out the requested collection tasks and submitted price data and related metadata using the application. The contributors were subsequently compensated based on the task location and degree of difficulty.

The collected price data covers 162 tightly specified items for a variety of household goods and services, including food and non-alcoholic beverages; alcoholic beverages and tobacco; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine household maintenance; health; transport; communication; recreation and culture; education; restaurants and hotels; and miscellaneous goods and services. The use of common item specifications aimed at ensuring the quality, as well as intra- and inter-country comparability, of the collected data.

In total, as many as 1,262,458 price observations―ranging from 196,188 observations for Brazil to 14,102 observations for Cambodia―were collected during the pilot. The figure below shows the cumulative number of collected price observations and outlets covered per each pilot country and month (mouse over the dashboard for additional details).

Figure 1: Cumulative number of price observations collected during the pilot

Artificial intelligence for smart cities: insights from Ho Chi Minh City’s spatial development

Ran Goldblatt's picture
Zoning by Land Parcel (Source: https://thongtinquyhoach.hochiminhcity.gov.vn)

It’s amazing to see what technology can do these days! Satellites provide daily images of almost every location on earth, and computers can be trained to process massive amounts of data generated from them to produce insightful analysis/information. This is just one of the demonstrations of artificial intelligence (AI). AI can go beyond just reading images captured from space, it can help improve lives overall.

For urban governance, machine learning and AI are increasingly used to provide near real-time analysis of how cities change in practice – for example, through the conversion of green areas into built-up structures. By teaching computers what to look for in satellite images, rapidly expanding sources of satellite data (public and commercial), together with machine learning algorithms, can be leveraged to quickly reveal how actual city development aligns with planning and zoning or which communities are most prone to flooding. This provides insights beyond the basic satellite snapshots and time-lapse visualizations that can now be readily generated for any areas of interest.

But the barriers to applying these technologies can still seem daunting for many cities around the world. It’s not always clear how exactly to analyze this massive amount of satellite data, nor how to get access to it.

If you know what stakeholders really think, can you engage more effectively?

Svetlana Markova's picture

The World Bank Group surveys its stakeholders from country governments, development organizations, civil society, private sector, academia, and media in all client countries across the globe. Building a dialogue with national governments and non-state partners based of the data received directly from them is an effective way to engage stakeholders in discussions in any development area at any possible level.

Let's take the education sector as an example to see how Country Survey data might influence the engagement that the Bank Group has on this highly prioritized area of work.

When Country Surveys ask what respondents identify as the greatest development priority in their country, overall, education is perceived as a top priority (31%, N=263) in India.1 However, in a large country, stakeholder opinions across geographic locations may differ, and the Country Survey data can be 'sliced and diced' to provide insight into stakeholders' opinions based on their geography, gender, level of collaboration with the Bank Group, etc. In India the data analyzed at the state level shows significant differences in stakeholder perceptions of the importance of education. The survey results can be used as a basis for further in-depth analyses of client's needs in education in different states and, therefore, lead to more targeted engagement on the ground. In the case of the India Country Survey, the Ns at the geographical level may be too small to reach specific conclusions, but this example illustrates the possibility for targeted analysis.

Tracking Urbanization: How big data can drive policies to make cities work for the poor

Axel van Trotsenburg's picture

Every minute, dozens of people in East Asia move from the countryside to the city.
The massive population shift is creating some of the world’s biggest mega-cities including Tokyo, Shanghai, Jakarta, Seoul and Manila, as well as hundreds of medium and smaller urban areas.

This transformation touches on every aspect of life and livelihoods, from access to clean water to high-speed trains that transport millions of people in and out of cities during rush hour each weekday.