# Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data

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## Abstract

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^{2}was 0.78. (3) Our framework shows great potential in mapping the population in real time. Our findings expand the knowledge in estimating urban population. In addition, the proposed framework and approach provide an effective means to quantify population distribution data for cities, which is particularly important for many of the cities worldwide lacking census data at the residential neighborhood scale.

## 1. Introduction

## 2. Study Area and Data

#### 2.1. Study Area

^{2}, and it contains 16 districts (equivalent to level 3 of the Global Administrative Unit Layer defined by the Food and Agriculture Organization) and 325 subdistricts (equivalent to level 4 of the Global Administrative Unit Layer). The population data from the government have relatively coarse temporal and spatial resolutions. Two types of population data were generated. The first type of data is from a population census every 10 years at the subdistrict level, and the second type is from a population sampling survey conducted every year at the country level. From the population sampling survey in 2018, the permanent population was estimated to be 21.54 million and the population density was 1313 people per square kilometer (Beijing statistical yearbook, 2019). Beijing has a typical concentric circle structure because of its long history. The population density follows a roughly decreasing trend from the center to the suburbs. The fifth-ring area is the core area of Beijing and is the most densely populated area containing nearly half of the population in 4% of the area (Beijing Municipal Bureau of Statistics, 2016). Although the fifth-ring area is a deeply rooted concept that has been embedded in residents’ lives and one of the most popular study areas, the population survey data from 2015 are the only record of this area because of the mismatched boundary between the fifth-ring area and the administrative unit. Therefore, we selected the fifth-ring area in Beijing as our study area (Figure 2).

#### 2.2. Data and Processing

^{TM}(one of the most popular real estate trade platforms in China, https://bj.lianjia.com/). Then, the number of households was linked to population-related factors based on location.

## 3. Methods

^{2}) value from the OLS results. After correlation analysis, we mapped the urban population at a fine scale using two popular estimation approaches: statistical modeling approach and dasymetric mapping. Finally, the mapping accuracy of the population was assessed.

#### 3.1. Relationships between Population and Related Factors and Their Relative Importance

^{2}was used to measure the robustness of the regression model and the capability of the related factor to explain the population.

^{2}from the OLS regression estimation can also be used to assess the relative importance of related factors as an auxiliary index.

#### 3.2. Mapping the Population

#### 3.3. Accuracy Assessment

^{2}, mean square error (RMSE), and mean absolute percentage error (P), which are well-known accuracy assessment metrics of population mapping [1,18,47,48], were selected to comprehensively assess the accuracy at the residential neighborhood scale. The R

^{2}is from a zero-intercept regression. The RMSE and P are calculated using the estimated population from the population mapping model and the actual population. The formulas for these indexes are as follows,

## 4. Results

#### 4.1. Relationships between Population and Population-Related Factors

#### 4.2. Relative Importance of Population-Related Factors

^{2}values from the OLS regressions are shown in Figure 3. A similar highest importance of building features and similar lowest importances for distance and length of roads were also found for the indexes. Floor area has the highest R

^{2}, which was the same for %IncMSE and IncNodePurity.

#### 4.3. Population Accuracy and Methods Comparison

^{2}for a zero-intercept regression and a lower RMSE and P than dasymetric mapping (Figure 5). The P of the statistical modeling approach was 15.27% lower than that of the dasymetric mapping approach. However, we found that dasymetric mapping was slightly closer to 1 than the statistical modeling approach.

^{2}values from the zero-intercept regressions were 0.86 and 0.87 for households and population, respectively. P was approximately 33%. In addition, the RMSE was 12,273 for the number of households and 37,868 for population, respectively.

#### 4.4. Distribution Features of the Urban Population in the Fifth-Ring of Beijing

^{2}in the residential neighborhoods. Meanwhile, strong spatial heterogeneity can be found in the fifth-ring area. The distribution of the population density is similar to the distribution of the population. The highest population density occurs around the 3rd ring road, and relatively low population density occurs in 2nd ring area and 5th ring area.

## 5. Discussion

#### 5.1. Relationships between Population and Population-Related Factors

#### 5.2. Relative Importance of Population Related Factors

#### 5.3. Difference between Dasymetric Mapping and Statistical Modeling Approach

^{2}was 0.99 in Bhaduri et al. (2007) [13]). Therefore, it is time to reconsider the value of dasymetric mapping and its assumption at a fine scale.

#### 5.4. Real Time Updating of Population Using Statistical Modeling Approach

^{TM}map and Amap). NTL also has a short revisit period (one month).

#### 5.5. Population Data from Different Sources

#### 5.6. Prospects for Future Research

## 6. Conclusions

^{2}of 0.78. The accuracy increased at the subdistrict level, with a mean absolute percentage error of 33.1% and a R

^{2}of 0.87. (5) Our framework could be used to renew the population data in real-time.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 3.**Relationships between the related factors and population. Here, population is represented by the number of households in residential neighborhoods, which assumes that the population is proportional to the number of households. All of the variables are measured in natural logarithms so that they followed a normal distribution expect distance and the length of roads, which is normally distributed.

**Figure 4.**Percentage-increased MSE and IncNodePurity indicate the importance of variables in the RF regression model.

**Figure 5.**Comparison of the two mapping approaches at the residential neighborhood level. (

**a**) The accuracy of the statistical modeling approach. (

**b**) The accuracy of dasymetric mapping. The black lines are the zero-intercept regression lines.

**Figure 6.**Accuracy of statistical modeling approach at the subdistrict level. (

**a**) The number of residential households. (

**b**) Population. The black lines are the zero-intercept regression lines.

**Figure 7.**Population and population density distribution of the residential neighborhoods in Beijing’s fifth-ring area. (

**a**) Population in each residential neighborhood and (

**b**) population density (people/km

^{2}) at the residential neighborhood level.

Method | Spatial Resolution | References | |
---|---|---|---|

Global-scale | Areal interpolation (Dasymetric mapping) | 100–1000 m | Balk et al. 2006; Bhaduri et al. 2007; Leyk et al. 2019 |

National/Regional-scale | Areal interpolation (Dasymetric mapping) | More than 100 m | Li and Zhou 2018; Azar et al. 2013; Deville et al. 2014 |

Local-scale | Statistical modeling approach | Less than 100 m/block level | Dong et al. 2010; Silvan-Cardenas et al. 2010; Weber et al. 2018; Wang et al. 2019 |

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## Share and Cite

**MDPI and ACS Style**

Jing, C.; Zhou, W.; Qian, Y.; Yan, J.
Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data. *Remote Sens.* **2020**, *12*, 3235.
https://doi.org/10.3390/rs12193235

**AMA Style**

Jing C, Zhou W, Qian Y, Yan J.
Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data. *Remote Sensing*. 2020; 12(19):3235.
https://doi.org/10.3390/rs12193235

**Chicago/Turabian Style**

Jing, Chuanbao, Weiqi Zhou, Yuguo Qian, and Jingli Yan.
2020. "Mapping the Urban Population in Residential Neighborhoods by Integrating Remote Sensing and Crowdsourcing Data" *Remote Sensing* 12, no. 19: 3235.
https://doi.org/10.3390/rs12193235