metadata: authors: 0: author: Galdo,Virgilio
1: author: Li,Yue-000316086
2: author: Rama,Martin G.
count: India
docna: 0: docna: Identifying Urban Areas by Combining Data
            from the Ground and from Outer Space : An Application to India
docty: Policy Research Working Paper
subtopic: Employment and Unemployment,ICT Applications,Transport Services,Hydrology
teratopic: Information and Communication Technologies,Water Resources,Social Protections and Labor,Transport
abstracts: cdata!: This paper develops a tractable method
            to identify urban areas and applies it to India, where
            urbanization is messy. Google Earth images are assessed
            subjectively to determine whether a stratified large sample
            of Indian cities, towns and villages, as officially defined,
            are urban or rural in practice. Based on these assessments,
            a regression analysis combines two sources of
            information—data from georeferenced population censuses and
            data from satellite imagery—to identify the correlates of
            units in the sample being urban. The resulting model is used
            to predict whether the other units in the country are urban
            or rural in practice. Contrary to frequent claims, India is
            not substantially more urban than implied by census data.
            And the speed of urbanization is only marginally higher than
            official statistics suggest. But a considerable number of
            locations are misclassified in the midrange between villages
            and state capitals. The results confirm the value of
            combining subjective assessments with data from these
            different sources.
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repnme: repnme: Identifying Urban Areas by Combining Data
            from the Ground and from Outer Space : An Application to India
display_title: Identifying Urban Areas by Combining
            Data from the Ground and from Outer Space : An Application
            to India
listing_relative_url: /research/2018/10/30557010/identifying-urban-areas-combining-data-ground-outer-space-application-india
topicv3: Water m1326522 137,Transport m1326661 135,Population, Aging and Health m1327736 640,Census Data m1504073 2763,Open Source m1505699 2946
publishtoextweb_dt: 2018-10-29T00:00:00Z
historic_topic: Information and Communication Technologies,Water Resources,Social Protections and Labor,Transport
no_of_pages: 34
author_keys: 0: author_key: 1174529
1: author_key: 95769
model_extractions: WPS8628 Policy Research Working Paper 8628 Identifying Urban Areas by Combining Data from the Ground and from Outer Space An Application to India _Virgilio Galdo Yue Li Martin Rama_ South Asia Region Office of the Chief Economist October 2018
Policy Research Working Paper 8628

# **Abstract**

This paper develops a tractable method to identify urban areas and applies it to India, where urbanization is messy. Google Earth images are assessed subjectively to determine whether a stratified large sample of Indian cities, towns and villages, as officially defined, are urban or rural in practice. Based on these assessments, a regression analysis combines two sources of information—data from georeferenced population censuses and data from satellite imagery—to identify the correlates of units in the sample being urban.

The resulting model is used to predict whether the other units in the country are urban or rural in practice. Contrary to frequent claims, India is not substantially more urban than implied by census data. And the speed of urbanization is only marginally higher than official statistics suggest. But a considerable number of locations are misclassified in the midrange between villages and state capitals. The results confirm the value of combining subjective assessments with data from these different sources.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at yli7@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Identifying Urban Areas by Combining Data from the Ground and from Outer Space: An Application to India Virgilio Galdo, Yue Li, and Martin Rama

**Keywords:** urban extent; urbanization rate; subjective assessment; Google Earth images; georeferenced data; satellite imagery

**JEL Classification:** O1, O18, R1

Virgilio Galdo, Yue Li, and Martin Rama are with the Office of the Chief Economist for South Asia at the World Bank. The corresponding author is Yue Li. Her contact information is yli7@worldbank.org.

Skillful research analysis was provided by Maria Florencia Pinto, Sutirtha Sinha Roy, and Wenqing Zhang. Comments and suggestions by Gilles Duranton and by participants at the 8th European Meeting of the Urban Economics Association are gratefully acknowledged.

This research was funded by the World Bank and by the Department for International Development of U.K. as part of the Sustainable Urban Development Multi‐Donor Trust Fund.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the executive directors of the World Bank or the governments they represent.
# **1. Introduction**

What is a city? The most candid answer may be “I know one when I see it.” The subjectivity of the city concept is understandable because of the continuum between urban and rural spaces. The many terms used to describe this in‐betweensuburb, exurb, edge city, and urban sprawl, among otherstestifies to the absence of a clear divide. Just like beauty, a city is in the eyes of the beholder.

Official answers to this question are less straightforward, varying substantially across national governments and international organizations. A glimpse at this diversity is provided by World Urbanization Prospects, a comprehensive source of international urban data that builds primarily on official information. About 45 percent of the 232 countries reported in its 2014 revision used each country’s own administrative definition as the sole or primary criterion for distinguishing urban from rural. Some 27 percent used population size as the sole criterion, and another 25 percent adopted a combination of metrics. The remaining 3 percent of countries had no official definition (UN 2015). Moreover, many countries change their definitions of cities periodically.

Not surprisingly, this diversity of criteria results in a lack of consensus on which urban agglomerations should be considered cities. It is thus not clear how urban areas can be compared across countries and over time.

This seemingly trivial issue has important implications. The rural‐urban transformation drives economic development and is often intertwined with structural transformation. Because cities create prosperity by bringing together households and firms and making everyone more productive, the rural‐urban transformation is central to development economics, and cities are at the core of economic geography (Fujita, Krugman, and Venables 2001; Henderson 1974; Krugman 1991; Lewis 1954).

However, the difficulties in identifying urban areas have undermined empirical efforts to connect with the theoretical literature and even to establish the stylized facts with confidence. For example, it is widely accepted that a country’s urban population share is related to its income per capita. And it is also said that India’s urban population share is too low relative to its income level. However, some studies suggest that many areas of India are misclassified as rural by the official definition, and so the level of urbanization is underestimated (Denis and Marius‐Gnanou 2011; World Bank 2013).

This disconnect is unlikely to occur only in India. At the global level, the diversity of urban metrics could challenge regularities taken for granted. For example, it could well be that urbanization rates are more accurate in agrarian societies and in advanced economies but underestimated in between these two extremes. If so, the relationship between the urban population share and income per capita would have a different gradient relative to what is commonly accepted.

Recognition of this measurement problem has led to alternative approaches to defining the urban extent in ways that could ensure comparability across countries and over time. One approach is to rely on information from the ground, such as highly disaggregated data from population and economic censuses. An example of this more systematic approach is the paper by Rozenfeld and others (2011) on the Great Britain and the United States.

Another promising approach is the use of remote sensing technology to identify urban areas from outer space. A number of methods that rely on images taken by sensors aboard satellites have been proposed in this respect. Methods to identify cities from outer space can be broadly classified into three groups, 2
depending on their primary input data: (1) satellite images of land cover and of built‐up characteristics; (2) satellite images of areas lit up at night; and (3) gridded population data created by using satellite images and other auxiliary information to redistribute aggregated data from population censuses.

To many, these approaches hold the promise of cross‐country neutrality and comparability because they ensure global coverage, spatial granularity, and temporal consistency. But a consensus has yet to emerge on the most reliable method to identify urban areas. To date, then, the results have been heterogeneous. At the global level, the urban areas identified cover from less than 300,000 square kilometers to over 700,000 square kilometers, depending on the method (Potere and others 2009).

Some features of the input data and their interpretation may help explain the large variations in results when using these methods:

- Most analyses are conducted at the cell level, but a city is more than the sum of its cells. This remark goes back to the subjectivity of the concept of a city. The proper of an urban setting is the interlinking of cells in a broader spatial context. Assessing whether a cell is urban without referring to the surrounding cells is thus potentially misleading, and even more so when the urbanization process is messy (World Bank 2015).

- Analyses relying primarily on land cover classification algorithms can accurately identify built‐up areas and infrastructurethe signature characteristics of urban areas. But cities include other land cover types such as parks and water bodies. The continuum between rural and urban areas poses a challenge to these algorithms as well (Mertes and others 2015; Pesaresi, <mark>Ehrlich, a</mark> nd others 2016; Schneider 2012).

- Analyses relying primarily on nightlight data capture the concentration of human activities in an unprecedented manner. However, problems such as blooming lead to biased estimates of the urban extent (Elvidge and others 2007; Elvidge and others 2009; Li and Zhou 2017). As access to electricity becomes more common and reliable, the pace of urbanization could be misinterpreted as well.

- When using gridded population data, one fundamental issue is the lack of reliable information on disaggregated administrative boundaries. Open‐source boundaries are available mostly at the district or subdistrict level (GADM 2012). Population census data are at best georeferenced to these levels of administrative boundaries. And both population data and administrative boundaries tend to be out of date by several years.

To address these limitations, we develop a tractable method for identifying urban areas and illustrate its potential by applying it to India. The proposed method differs from the previous literature in three important ways:

- Instead of cells, we use _cities, towns and villages_ , as officially defined, as our units of analysis. Because the available boundaries of administrative units tend to be unavailable, out of date, or erroneous, we rely on newly available georeferenced and digitized boundaries of administrative units at all levels, based on the India’s _Administrative Atlas_  _2011_ (ORGI 2011b) **.** Although the analysis covers the entire universe of cities, towns, and villages, an in‐depth assessment is conducted for a stratified sample of them. The sample design results in roughly three‐quarters of the units being administratively urban and the rest administratively rural.

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- _Subjective assessments_ of images provided by Google Earth are used to determine whether the cities, towns and villages in the sample are urban or rural in practice. Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. Using this combination of perspectives, a layperson can judge the characteristics of a specific location. Google Earth images have been used to crowd‐source judgments on city safety, inequality, and gentrification (Hwang and Sampson 2014; Salesses, Schechtner, and Hildalgo 2013). They have also been used in the remote sensing literature to establish training sites and validation sites at the pixel level (Bontemps and others 2011; Schneider, Friedl, and Potere 2009; Yu and Gong 2012).

- A regression analysis combining _multiple sources of information_ is used to identify the correlates of units in the sample being urban in practice. We georeference population census data, which come from the ground, to all administrative boundaries in India. We also incorporate data from open‐source satellite imagery, which come from outer space, on both built‐up areas and nighttime lights. Bootstrapping and cross‐validation are applied to choose the model specifications with higher prediction accuracy. These model specifications and the estimated coefficients are then used to predict whether each of the other units in the country is urban or rural in practice.

This exercise allows a more accurate assessment of the rate and speed of urbanization in India than was available before. Contrary to frequent claims, we do not find that India is substantially more urban at present than stated by census data. And the speed of urbanization over the last decade is only marginally higher than official statistics suggest. At the same time, however, we find a considerable misclassification of locations in the midrange between villages and state capitals.

The analysis also has implications beyond India. We find that the accuracy of the assessment increases substantially when combining data from the ground and from outer space. In the process, it appears that some of the most frequently used indicators of urban status can yield misleading results. Among the indicators from the ground, population density is a poor predictor of urban status. Among those from outer space, nighttime light intensity leads to a drastic overestimation of the speed of urbanization.

# **2. A representative sample of units**

The official boundaries of cities, towns and villages are built on historical data and capture the best knowledge governments have of the spatial distribution of economic activities and people. Admittedly, these boundaries may be too small to account for large metropolitan areas, which typically include nearby suburban and exurban areas. But they define the constituency to which each local government provides services and is accountable. And they are the basic sample unit used by national statistical agencies to conduct censuses and surveys. Therefore, important information about cities, towns and villages can be captured at this level of spatial disaggregation.

## _The universe of administrative units_

In this study, we take advantage of newly digitized boundaries of administrative units in India, now available all the way down to the town or village level. Although not official, these boundaries are based on India’s _Administrative Atlas_  _2011_ (ORGI 2011b). They were generated as part of a broader research project, the Spatial Database for South Asia (Li and others 2015). Construction of the boundaries required 4
scanning and georeferencing 6,598 physical maps: 35 maps of states and union territories, 640 district maps, and 5,923 subdistrict maps.

The subdistrict maps present the administrative boundaries of towns and villages in their jurisdiction. The location, size, and shape of these towns and villages were digitized in the form of 649,818 boundary polygons. Attributions such as location codes, names, and administrative types were added to the polygons. This study takes advantage of these attributions. Information on the administrative types of the cities, towns and villages allows a refined stratification strategy for sampling.

Data on administrative boundaries are adjusted for this study in three ways. First, in the original database multiple polygons were retained for towns that extended over more than one subdistrict. In this study, those multiple polygons are merged into a single administrative boundary for each town. This results in 649,649 units whose boundaries have a one‐to‐one correspondence with administrative units.

Second, some villages were presented as points on the source maps and their boundaries were  unavailable, totally or partially they are mostly in the mountainous or forest areas of four states (Arunachal Pradesh, Chhattisgarh, Himachal Pradesh, and Meghalaya). These villages are excluded from the analysis. And, third, population information was not available in the 2011 Census of India for some villages, which raises concerns about the quality of the corresponding official maps. These villages are excluded as well.

As a result of these mergers and exclusions, 564,052 administrative units with well‐identified boundary polygons are retained as the universe for the analysis. However, for the purpose of extracting a sample of cities, towns and villages, the set of administrative units is further restricted to those belonging to the 21 largest states and union territories. These large states and union territories are covered by the high‐quality household surveys whose data are needed for a proper stratification. Focusing exclusively on them further reduces the number of administrative units considered to 561,624.

The total population of these 561,624 polygons in 2011 was 1,174 million, or 3.0 percent less than reported by the 2011 Census for the country as a whole (ORGI 2011). Almost a third of this population gapabout 11.3 million peopleis accounted for by the small states and union territories excluded from the analysis. Some of these small states and union territories are richer than the Indian average and some are poorer, which suggests that their exclusion from the analysis may not bias results much. The rest of the gap is associated with the villages excluded because their boundaries or population figures were unavailable. These villages are most likely rural in practice, and their population size is small about 200  people at the median which implies that they too are unlikely to be an important source of bias.

# _A two‐stage stratification process_

A two‐stage disproportionate stratification approach is used to select the sample of administrative units to be inspected visually (figure 1). In the first stage, the universe is divided into eight strata by administrative category, following the definitions of the 2011 Census of India. Five of these categories state capitals, other municipal corporations, municipalities, industrial towns, and other townsare statutorily designated as urban. All state capitals are governed as municipal corporations; we use the term other municipal corporations to refer to the rest of the cities in this administrative category. Two of the   categories census towns and outgrowths are statutorily rural but recognized by the 2011 Census as 5
<!-- Start of picture text --> N = 649,649 N = 564,052 N = 561,624 n* =1277— =y<br>All city/ town :<br>administrativevillage-level / .; ;<br>units : - Ae<br><!-- End of picture text --> <u>2 iC — pi)</u> 2 ~~(2~~ <u>iG=po</u> ii ii i i i
**Table 1. The sample and its distribution across administrative categories**

||N|Margin of<br>error|Prior estimate on<br>the probability of<br>being urban|n*|
|---|---|---|---|---|
|Capitals and Municipal Corporations|147|0.05|0.97|34|
|Municipalities|905|0.05|0.90|120|
|Industrial Towns|31|0.05|0.90|25|
|Other Towns|2,769|0.05|0.80|226|
|Census Towns|3,710|0.05|0.71|292|
|Outgrowths|935|0.05|0.60|264|
|Villages|553,127|0.03|0.10|315|
|Total|561,624|||1,277|
|Mean probability of being urban|||0.11||
|Relative standard error|||0.08||

In equation (1) the term e� is the margin of error allowed for administrative category i , and z indicates the boundaries of the chosen confidence interval. For a conventional 95 percent confidence, the value of z is 1.96, and e� is equivalent to 1.96 times the standard error of p� . We assume that the standard error of the prior probability of being urban is much smaller for villages than for the seven other administrative categories. Therefore, e� is set at 3 percent for villages and at 5 percent for all other strata.

Based on these assumptions, the total sample size needs to be 1,277 or higher: 405 units statutorily urban; 557 units urban in terms of the census; and 315 villages. For the full sample we achieve a relative standard error of 8 percent, which is below the 10 percent threshold required for a survey design to be considered good (UN 2005).

In the second stage, each of the eight strata is divided into three substrata defined by average consumption per capita. Consumption data are from the household consumer expenditure module of the 68th round of National Sample Survey of India (NSSO 2012). The data quality is considerably better for the 21 largest states and union territories than for the smaller ones. The monthly household expenditure is divided by household size to estimate consumption per capita. To account for the spatial variation in prices, the result is deflated by a year‐specific price index that differs across states and union territories and between rural and urban areas. The 21 states and union territories are ranked on the basis of their average consumption per capita, and they are divided into three groups of equal size: richer, intermediate, and poorer.

The sample for each administrative category is split evenly across these three groups of states and union territories. For example, of the 264 units retained in the outgrowth stratum of the sample, 88 would be from richer states and union territories, another 88 units from the intermediate group, and the remaining 88 from the poorer group.

The sampling process is completed by conducting random draws without replacement for each of the 24 substrata. Our final sample with 1,277 administrative units is relatively evenly distributed across India’s 7
 territory (map 1). These selected units cover five of the six regions of the country northern, central,  eastern, western, and southern relatively well. Only for the northeastern region is the coverage of the sample poor. This is due to the shortcomings of village boundaries in the states of Arunachal Pradesh and Meghalaya and the low quality of the household surveys in the states of Manipur, Mizoram, Tripura, Meghalaya, and Assam.

**Map 1. The spatial distribution of sample units**

# **3. The urban status of units in the sample**

Cities have diverse characteristics both across and within countries. India has a continuum of urban and rural spaces, ranging from a glamorous metropolis such as Mumbai, capital of Maharashtra, to a tiny shabby town such as Bagula in West Bengal, to any among hundreds of thousands of villages. An objective method based on one or two metrics with fixed thresholds for distinguishing between urban and rural areas may not adequately capture this diversity. A richer combination of criteria would help describe the multifaceted nature of a city’s environment, but the joint interpretation of these criteria would invariably involve an element of judgment. Building on this intuition, we rely on subjective assessments to determine whether each administrative unit in the sample is urban or rural in practice.

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# _A subjective but structured process_

Subjective assessments have been used in the economic literature to complement more objective approaches. For example, in measuring individual well‐being a subjective assessment has been adopted on the grounds that everybody has his or her own views on what a good life looks like, and what makes a good life may touch on dimensions for which no reliable indicator is available (Frey and Stutzer 2002; Veenhoven 2004). Deciding whether a specific place is a city poses challenges similar to those posed by measuring well‐being.

For a subjective assessment to be thorough, researchers should ideally visit each city, town, and village in the sample and “see” how they look like. However, the time and cost implications of visiting 1,277 locations scattered across a large country are dissuasive. Building on the literature, we adopt a second‐ best approach, which is to rely on high‐quality, open‐source images by Google Earth.

Google Earth is an integrated collection of processed satellite, aerial, street view, and 3‐D images. A zoom‐ in option is available, and ground views and different time views are also available whenever possible. This combination of perspectives enables a layperson to judge the characteristics of a specific location. No special training in remote sensing is needed.

We overlaid the digitized boundary of each sample unit on top of Google Earth images and conducted a subjective assessment of the images falling within the corresponding boundary. For the vast majority of units, we rely on images dated as having been taken in 2010–12 to be consistent with the population data from the 2011 Census of India. However, when the images for these three years are of poor quality, we complement them with images from other years.

The sample of cities, towns, and villages was randomly split into three subsets, which were in turn allocated to three research analysts. All research analysts relied on the same decision tree to independently classify the units allocated to them as urban or rural.

The common decision tree considers characteristics observable from Google Earth images: distribution of land cover types, relationship between buildings, characteristics of buildings, prevalence of transportation networks, and availability of amenities (schools, universities, hospitals, cultural sites, etc.). The selection of characteristics for the decision tree builds on the economic geography and urban economics literature:

- The simple monocentric model of land use predicts that the density of _construction_ declines as one moves away from a city center to more rural areas (Alonso 1964; Mills 1967; Muth 1969). Extensions of this model dealing with polycentric cities and scattered development have not fundamentally contradicted this prediction (Bueckner 1987; Duranton and Puga 2015). We thus expect both the share of built‐up area and the density of buildings to be higher in urban areas.

- Lumpy _infrastructure_ investments facilitating access to markets are critically important in giving rise to the concentration of economic activities (Duranton and Puga 2004; Fujita, Krugman and Venables 2001; Krugman 1991; Scotchmer 2002). We therefore expect both inter‐ and intracity transport networks to be more visible in urban areas.

- Services and _amenities_ are crucial in explaining where firms prefer to operate and households prefer to live. The self‐selection of households across locations accounts for 40–50 percent of productivity differences across cities (Ahlfeldt and others 2015; Combes and others 2010; Straszheim 1987). A

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According to the subjective assessments, 49 percent of the 1,277 units in the sample are urban in practice, and 51 percent are rural (table 2). Therefore, the sampling strategy does ensure a balanced representation of rural and urban units. At the same time, the exercise reveals substantial differences between the administrative classification of units in the sample and their subjective assessment as urban or rural. Discrepancies are greater for units deemed urban in terms of the census, many of which are reassessed as rural. A nonnegligible fraction of statutorily urban units is also reclassified as rural. But there are no substantial differences for villages, whose rural status is almost consistently confirmed by the subjective assessments.

**Table 2. Subjective assessments versus administrative classification**

|Administrative classification|Assessed urban<br>(percent)|Assessed rural<br>(percent)|Total<br>(percent)|
|---|---|---|---|
|Statutory urban units|24.9|6.8|31.7|
|Census urban unit|24.0|19.6|43.6|
|Census rural unit|0.4|24.3|24.7|
|Total|49.3|50.7|100.0|

In light of these discrepancies, a relevant question is whether the size of the sample is large enough to trust the results. In setting the sample size, we relied on the Central Limit Theorem, which implies an asymptotic normal distribution for the statistic of interest. But with a relatively small sample size, the distribution could be different. One way to address this concern is to use the bootstrap method, which generates an approximation of the distribution through a Monte Carlo simulation, in which the sampling is carried out from the fitted distribution of the observed data (Cameron and Trivedi 2005).

The bootstrap method is applied to the sample of 1,277 units, for which we know the urban status based on both the administrative classification and subjective assessment. In each replication, we randomly draw units from this sample with replacement, until arriving at the same sample size as before (1,277). The drawing is done independently for each administrative stratum. For the new sample obtained in each replication, the share classified as urban is computed on the basis of the subjective assessments. After replicating 10,000 times, we estimate the distribution of this urban share, including its mean, standard errors, and confidence intervals (figure 3).

The bootstrap analysis confirms that the sample is sufficiently large for each administrative stratum. For capital cities and municipal corporations, as well as for villages, bootstrap standard errors are below 1 percent. At 5 percent, the standard error is larger for industrial towns, reflecting the small size of the sample for this stratum. However, because the sample includes 25 of 31 units in this stratum, the sampling does not affect the accuracy of the results. Standard errors are below 3 percent for all other administrative strata.

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urban extent is a challenge across the world, and results that are internationally comparable is the goal. This requires focusing on a set of key indicators available for the vast majority of countries.

At the same time, the set of indicators chosen is broader than is generally the case when measuring the urban extent. Official estimates of urbanization rates by national governments and international organizations rely almost exclusively on data from the ground. The key indicators considered are population size, population density and administrative category. Conversely, products recently developed by the remote‐sensing literature focus almost exclusively on data from outer space. Built‐up cover and nighttime lights are the preferred indicators in this respect. But rather than choosing one set of indicators or the other, our analysis combines data from the ground with data from outer space.

Data from the ground are taken from the Spatial Database for South Asia, which georeferenced both the 2001 and 2011 Census of India to the digitized official boundaries of administrative units (Li and others 2015). A range of georeferenced indicators from the ground are available for 2001 and 2011 across all units in the country. The indicators are population size, population density, employment structure, access to services, dependency ratio, and literacy rate. The administrative type of each unit is also available, as described earlier.

Following the practice of national governments and international organizations, we use _population size_ and _population density_ at the town and village level as our explanatory variables from the ground. This choice is consistent with that of studies for more advanced economies that rely on high‐quality microdata from the ground (Michaels, Rauch, and Redding 2012; Rozenfeld and others 2011). Population density is computed as the ratio between the population of the administrative unit and the size of the area within the corresponding polygon boundary. In some specifications, we also consider the units’ _administrative type_ as defined by the 2011 Census.

Data from outer space are selected building on the remote sensing literature, as well as on recent developments in the economics literature that use satellite imagery (Burchfield and others 2006; Donaldson and Storeygard 2016; Henderson, Storeygard, and Weil 2012; Jean and others 2016). Our indicators are derived from the best open‐source satellite imagery available for 2001 and 2011, which are the years when population was counted. The indicators are computed by overlaying the boundaries of towns and villages on the processed built‐up or nighttime light imagery.

In line with the literature, we choose _built‐up share_ and _lit‐up share_ at the town or village level as the two key indicators from outer space. Built‐up share is computed as the ratio between the built‐up area in a specific polygon boundary and the total area of the polygon. Lit‐up share is the share of the surface of the polygon whose nighttime light intensity exceeds some critical threshold. In some specifications, _built‐up area_ and _lit‐up area_ without dividing by the surface of the polygonare used as alternative indicators from outer space.

The assessment of land cover has improved rapidly over the last decade thanks to more precise spatial, spectral and radiometric resolutions of satellite imageries, and to much greater computational power to process the data. Three open‐source products on built‐up cover stand out as providing the best quality at the global scale:

- _MODIS_ relies on images taken by sensors aboard satellites from the Earth Observing System program.  

- These images known as the Land Cover Type Yearly Grid are provided by the Land Processes Distributed Active Archive Center managed by the U.S. National Aeronautics and Space

14
Administration. The product has a resolution of about 500 meters per cell and is available from 2001 onward (Friedl and others 2010).

- _GHSL‐Landsat_ is derived from images taken by sensors aboard satellites that are part of the Landsat program. As one of the first civilian satellite programs on land cover, Landsat has produced the longest continuous space‐based record of the Earth’s land surface. Data for the years 1975, 1990, 2001, and 2013/14 are available from the Global Human Settlement Layer project (Pesaresi, <mark>Ehrlich,</mark> and others 2016). The resolution of the product is about 38 meters per cell.

- _GUF‐TerraSAR_ is based on radar images taken by sensors aboard the twin satellites TerraSAR‐X and TanSAR, which are part of the more recent WorldDEM program. Reliance on radar images implies lower sensitivity to weather conditions, cloudiness and vegetation density. Data for 2011 are available from the German Aerospace Center at a resolution of about 12 meters per cell (Esch and others 2017).

After a careful comparison of the three data sources, we selected the GHSL‐Landsat product for this study. This choice is consistent with that of Burchfield and others (2006), who use the Landsat product to study urban development in the United States.

In the case of India, MODIS is limited by its coarse resolution. In particular, it does not capture built‐up cover in forest areas as well as the other two data sources. Data from both GHSL‐Landsat and GUF‐ TerraSAR yield very similar results while GUF‐TerraSAR has a higher resolution. The correlation coefficient between the two sources for the built‐up share at the town or village level is 0.96. In addition, GHSL‐ Landsat data are available for multiple years, whereas GUF‐TerraSAR data are available only for 2011. This is a constraint, because we want to estimate not only the urban extent, but also the speed of urbanization over time. We use GHSL‐Landsat data for 2001 and 2013/14 for the census years 2001 and 2011, respectively. But we check that the results remain the same when using GUF‐TerraSAR data for the year 2011.

For the measurement of the lit‐up area we rely on the Global Radiance Calibrated Nighttime Lights product. This product is derived from images taken by the Operational Linescan System (OLS) sensors aboard satellites from the U.S. Air Force Defense Meteorological Satellite Program (DMSP). Data are available from the National Geophysical Data Center at the National Oceanic and Atmospheric Administration (NOAA).

This is not the only product available for nighttime light images, but it has a higher quality than its competitors because it captures stable lights, addresses the saturation problem, and is intercalibrated to reduce inconsistencies across satellites (Elvidge and others 2009; Hsu and others 2015; NOAA 2014). The intensity of nighttime light is reported in Digital Numbers. We use data for 2001 and 2011, thereby matching the census years. Because the data are not generated in the same way in both years, we apply the intracalibration formula proposed by Hsu and others (2015) to ensure consistency over time.

A key challenge when using data on nighttime light is that even areas without human activity may appear to be lit up (Henderson and others 2003; Li and Zhou 2017; Zhou and others 2014). This may be due to blooming, or to the reflection of light from surrounding water and other land covers, or to the sensitivity of the sensors, or to the cumulative effects of geo‐referencing errors made in the capture and composting of satellite imagery. To avoid overestimation of the lit‐up share, a threshold is generally set up for nighttime light intensity, and only observations above the threshold are considered urban. However, the 15
<!-- Start of picture text --> 60<br>" °<br>50<br>ss 40<br>}<br>2<br>=<br>= 30 8<br>“ao5 2010 we e y= 0.0008x+ 13.821<br>.° R2 = 0.8404<br>°<br><!-- End of picture text --> <!-- Start of picture text --> io,<br>0<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>GDP per capita (USS at 2010 prices)<br><!-- End of picture text -->
**Table 3. Summary statistics of key indicators from the ground and from outer space**

||Obs|Mean|Std. Dev.|Mean|Std. Dev.|
|---|---|---|---|---|---|
|||2|001||2011|
|**Sample**||||||
|Population size (_thousands_)|1,277|38.73|371.39|46.79|392.48|
|Population density (_people per square_<br>_kilometer_)|1,277|2252|3983|3085|6057|
|Built‐up share (_percent_)|1,277|9.91|17.46|13.05|19.99|
|Lit‐up share (_percent_)|1,277|34.06|41.92|52.97|45.45|
|Built‐up area (_square kilometers_)|1,277|1.65|10.80|2.07|11.84|
|Lit‐up area (_square kilometers_)|1,277|4.74|20.87|6.83|23.24|
|**Universe**||||||
|Population size (_thousands_)|564,052|1.75|27.59|2.10|33.53|
|Population density (_people per square_||||||
|_kilometer_)|564,052|580|2339|693|2700|
|Built‐up share (_percent_)|564,052|0.50|3.47|0.81|4.43|
|Lit‐up share (_percent_)|564,052|2.57|14.18|8.57|25.93|
|Built‐up area (_square kilometers_)|564,052|0.04|1.13|0.05|1.35|
|Lit‐up area (_square kilometers_)|564,052|0.13|2.46|0.40|3.09|

# _A model for the probability of being urban_

Following the data modeling tradition, we specify an explicit probabilistic model on whether an administrative unit is urban or rural in practice: where U� is the subjective assessment of unit i , p� is the expected likelihood that a unit is urban given the values of its key indicators X� , and F�. � is the cumulative distribution function. The estimation of equation (2) requires that a functional form be assumed for F�. � . Here we use the standard logit model linking the urban status of a unit in a specific year with the values of its key indicators at a similar point in time. The justification for this choice, as opposed to the also standard Probit model, is its higher goodness of fit and better prediction accuracy. The results obtained using the Probit model are available upon request.

A linear function of X� is assumed for the function F�. � . The benchmark specification combines data from the ground and from outer space using population size, population density, built‐up share, and lit‐up share as the key indicators. The model is also run retaining only indicators from the ground or from outer space, either individually or combined (table 4).

17
**Table 4. Logit regressions on the probability of being urban**

||Benchmark<br>specification|D|ata from the grou|nd|Da|ta from outer sp|ace|
|---|---|---|---|---|---|---|---|
|Population|0.108***<br>(0.009)|0.104***<br>(0.008)||0.0925***<br>(0.008)||||
|Population density|1.63e‐05<br>(0.000)||0.00034***<br>(0.000)|0.000146***<br>(0.000)||||
|Built‐up share|0.0725***<br>(0.009)||||0.147***<br>(0.011)||0.106***<br>(0.010)|
|Lit‐up share|0.0213***<br>(0.002)|||||0.0276***<br>(0.002)|0.0169***<br>(0.002)|
|Pseudo R2|0.50|0.26|0.13|0.28|0.30|0.23|0.35|
|Log likelihood|‐442|‐658|‐773|‐636|‐622|‐682|‐573|
|Bayesian information criterion|920|1330|1560|1293|1257|1379|1167|
|Correctly classified|84.3|72.9|70.2|75.9|80.0|75.4|80.7|

_Note:_ Standard errors are reported in parentheses, with one, two and three asterisks indicating significance at the 10, 5 and 1 percent level.

18
<!-- Start of picture text --> 90 0<br>me}<br>vw<br>% 85 . ; -100<br>a Ke =. a<br>oC * NS, oe na a -200<br>i I, a eee _— -300 -<br>® 75 Nese a on en eee<br>6 k “ay! = ee —— 400;<br>2 70 a aan ;<br>o SNS agg tenes oot we<br>8 60 Nee ee ee -700<br>° ~~ - on<br>6 55 we -800<br>RS ,<br>50 -900<br>Benchmark Population Population Population size Built-up Lit-up Built-up share<br>specification size density + population share share + lit-up share<br>density<br>—e— Percent of observations correctly classified<br>—t— Log likelihood<br>------- Confidence Interval based on bootstrap method<br><!-- End of picture text -->
<!-- Start of picture text --> 100 1,800<br>#95 - 1,600<br>oSo 90 yea weer MoeNN— nee ecccccccccccne7eae ee 1,400 2a<br>6 OL. OO a 1,000 %<br>oO % SS yee" ee. oa oO<br>5 mo ON Se at . g00 6<br>e % = a od 600<br>2 70 SN all £<br>3 Pc oa 400 <<br>° “Seo<br>se «65 200<br><!-- End of picture text --> <!-- Start of picture text --> 60 0<br>Benchmark Population Population Population size Built-up Lit-up Built-up share<br>specification size density + population share share + lit-up share<br>density<br>—*— Percent of observations correctly predicted<br>—*— Akaike information criterion<br>------- Confidence Interval based on bootstrap method<br><!-- End of picture text -->
available upon request, confirm that the best results overall are associated with the benchmark specification.

# _Robustness checks_

A series of robustness checks confirms that combining data from the ground and from outer space leads to both a higher goodness of fit and better prediction accuracy than any of the two sets of indicators taken separately. The checks include:

- _Alternative indicators for data from outer space_ . When built‐up share is replaced by built‐up area and lit‐up share by lit‐up area, the performance of the model on the probability of being urban declines in terms of both goodness of fit and prediction accuracy.

- _Dummies for administrative type_ . The introduction of dummies only slightly improves goodness of fit and prediction accuracy. For example, the percentage of units correctly predicted in the benchmark specification increases by a mere 0.7 percentage points.

- _Nonlinear probability function_ . When a quadratic term is introduced for each of the four key indicators, the goodness of fit, as expected, increases. This comes at the cost of out‐of‐sample bias because of overfitting, as shown by the shrinkage indicator.

- _Blending of specifications_ . Following the “ensemble” method proposed by the statistical learning literature, we consider that a unit is urban if it is predicted to be so by at least four of the seven specifications. The percentage correctly predicted does not improve relative to the benchmark specification.

- _Inconclusive subjective assessments_ . Because research analysts were unable to unambiguously classify 46 units in the sample, we reestimate the probabilistic model removing these observations, and the results barely change.

- _Potentially inaccurate polygons_ . The subjective assessments suggested that the polygons for a few unitsmainly outgrowths and villagesmay not be appropriately geo‐referenced. We had replaced these polygons when compiling the sample of 1,227 units. A subjective assessment for these units and a prediction of their status using the regression model reveals that the out‐of‐sample prediction results are consistent with the subjective assessments.

These robustness checks, whose results are available upon request, give us confidence that the benchmark specification of the probabilistic model is a credible instrument for predicting the urban status of the broader universe of administrative units in India.

We also run a robustness check based on a different approach to statistical learning. In the analyses described earlier, a stochastic model for the data generation process was explicitly assumed. But a literature has emerged recently that dispenses with this assumption and proposes algorithmic modeling. This approach has gained prominence over last the two decades thanks to the rapid increase in computing power and the availability of big data. In computer science, the method is known as machine learning. Algorithmic modeling uses tools such as classification trees, in which data are split into groups at various nodes, each defined by a threshold for an explanatory variable. Compared with data modeling, these tools do not lend themselves to straightforward interpretation. But they can offer higher prediction accuracy (Breiman 2001b; James and others 2013).

21
The random forests method we use works as a combination of individual classification trees. Each of these trees focuses only on a subset of the explanatory variables to develop its nodes. That way, the classification trees are not correlated and offer different information on the underlying data generation process (Breiman 2001a; James and others 2013). Implementation of the random forests method is based on the package provided by Liaw and Wiener (2002) for the R program. As before, to compute prediction accuracy we split the sample between a training set and a validation set and repeat the exercise 10,000 times to estimate the mean and the confidence interval of the relevant metrics.

The benchmark specification under the logit regression framework performs as well as the random forests method, and the results from the two methods are highly correlated. The share of out‐of‐sample units correctly predicted when using the random forests method is 85.2 percent, or only 1 percentage point higher than when using the benchmark specification. This difference is less than the estimated standard error, which is around 1.4 percent under both the benchmark probabilistic model and the random forests method.

# **5. The rate and speed of urbanization**

The probabilistic model built from the sample of 1,277 units using subjective assessments can be used to predict the urban status of all 564,052 units in the universe of India in 2011. The key indicators for each unit are plugged into the model to compute the unit’s likelihood of being urban. If this likelihood is above 0.5, the unit is deemed urban; otherwise, it is considered rural. Combining indicators from the ground and from outer space, this procedure yields an urbanization rate that is almost identical to that in the 2011 Census of India. However, this similarity hides important discrepancies across administrative types, especially in the midrange between villages and municipal corporations. The exercise also shows that relying exclusively on one type of indicators, such as nighttime light, can yield potentially misleading results for India’s rate and speed of urbanization.

## _The urbanization rate in 2011_

According to the 2011 Census of India, the share of the population living in urban areas was 31.2 percent. But this figure is computed for a set of units broader than our universe because we excluded small states and union territories as well as villages for which information was missing. If only the 564,052 units considered in our analyses were retained, the 2011 Census of India would yield an urbanization rate of 31.6 percent. This is almost identical to the 31.4 percent rate predicted by the benchmark specification of the probabilistic model (figure 7).

Predictions using other specifications vary, but they also fall within a close range of the official estimate by the 2011 Census of India. The urbanization rate varies between 27.0 percent using built‐up share as the only key indicator and 33.9 percent using only population size. The predicted urbanization rate is similar31.8 percent when using the random forests method.

22
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<!-- Start of picture text --> 5<br>0<br>Census of Benchmark = Population Population Population size Built-up share —Lit-up share Built-up share + Random<br>India specification size density + population lit-up share forests<br>density method<br><!-- End of picture text -->
<!-- Start of picture text --> 120 —<br>ee Ase<br>100 Municipal corporations<br>2 o we<br>€ S 80 rie<br>=e Capitals ~~ a)<br><@ en<br>Og _@ Other towns<br>BY 60 @<br>as ~~ Municipalities<br>g a 40 Villages we ai<br>25 at Census towns<br>20 + Industrial we<br>towns<br>0 _@ Outgrowths<br>0 20 40 60 80 100 120<br>Urban population (millions),<br>2011 Census of India<br><!-- End of picture text -->
<!-- Start of picture text --> 100<br>><br>=ve _— 80<br>3 2 60 ——Capitals<br>BE<br>a _ a0 = =—Municipal corporatio<br>Es 20<br>2 = Industrial towns<br>g 3 Municipalities<br>* Ss -20<br>2 1s) > Other towns<br>© a -40<br>Qo<br>ER -60 ==TM—Census towns<br>2<br>-80 —eOutgrowths<br>-100 -e-Villages<br>Benchmark Population Population Population Built-up Lit-up Built-up Random<br>specification size density size + share share share+ lit- forests<br>population up share method<br>density<br><!-- End of picture text -->
<!-- Start of picture text --> 35<br>-*—Census of India<br>33<br>Se -—eBenchmark specification<br>2 31 ——Population size<br>; a<br>5<br>29<br>p= Population density<br>= —=—Population size + population density<br>2oO 27 -*Built-up share<br>a<br>95 -<Lit-up share<br>-©-Built-up share + lit-up share<br>23 —Random forests method<br>21<br>2001 2011<br><!-- End of picture text -->
would be higher in both 2001 and 2011. The speed of urbanization would also be the same in both years when focusing only on the built‐up share, but the urbanization rate would be much lower than using the benchmark specification. And if the lit‐up share were the only key indicator used, the change in the urbanization rate over a decade would be 11.3 percentage points, which is more than double the estimate derived from applying the preferred specification to the probabilistic model.

# **6. Conclusion**

The analysis in this paper yields a more accurate picture of urbanization in India than was previously available. We confirm that India’s urbanization rate in 2011 was close to that reported by the Census of India. However, this finding contrasts sharply with those of several studies reporting much higher urbanization rates (Denis and Marius‐Gnanou 2011; IDFC Institute 2015; Uchida and Nelson, 2009; World Bank 2015). We also find substantial gaps between the official classification and our results for several administrative categories of the census. Overall, the misclassification affects 62 million people, which is roughly equivalent to the population of France or the United Kingdom.

Among statutory urban categories, misclassification is highest for other towns. Although a large share of them have sufficient urban characteristics, many should be considered rural. The misclassification rate is much lower for villages. But given that there are more than half a million of them, the absolute number of misclassifications has non‐trivial implications.

Another important finding concerns the so‐called census towns of India, which are administratively rural but meet three criteria to be identified as urban areas according to the population census. In line with the population census and a growing literature, we confirm that most census towns are urban in practice. But this is not true of all of them.

Finally, our analysis shows that relying only on indicators from the ground or from outer space results in substantially different rates and paces of urbanization. For example, if the lit‐up share were the only key indicator used, the change in India’s urbanization rate over a decade would be 11.3 percentage points, which is more than double the figure obtained by combining data from the ground and from outer space.

Although this analysis centers on India, it has broader implications. Several recent studies have made a special effort to link the identification of urban areas more closely to the functionalities of cities. Examples of this new generation of studies are those by Davis, Dingel, and Miscio (2018); de Bellefon and others (2018); and Vogel and others (2018). This paper adds to this literature.

Our results show that there is value in relying on what one “sees,” especially where urbanization is messy. Subjective assessments can capture the multifaceted nature of cities—relatively large spaces with a higher density of construction, better access to transportation networks, and greater availability of residential amenities. In this paper, we develop what we believe is a credible structured approach to the subjective assessment of cities.

We also confirm the importance of combining indicators from the ground and from outer space when assessing the urban status of a location. Putting different sources of data together and comparing their performance in identifying urban areas reveals the weakness of some of the key criteria used traditionally. Population density, one of the workhorses of administrative approaches, performs quite poorly. And there are also pitfalls in relying exclusively on nighttime light, a method that is popular nowadays.

27
Finally, the value added from combining multiple sources of information highlights the importance of  reliable administrative boundaries. Digitized administrative boundaries the polygons delimiting the  jurisdiction of cities, towns, and villages are the true anchor of data integration. They can be used as the basis for combining information from different sources and for establishing ground truth. But for this approach to work, credible digitized files (often in the form of shapefiles) are needed for the lowest layers of the administrative hierarchy of a country. However, this issue is often overlooked by modern approaches based on remote sensing technology.

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