AI创作问答(高级版)

摘要：公共服务设施（POI）是城市中心地理空间结构的重要标志之一。本文介绍了一种基于POI和夜间灯光的城市中心识别及人口估算方法。首先，我们利用夜间灯光数据识别出城市中心区域。然后，我们使用POI数据来进行人口估计。实验表明，该方法能够有效地辨别出不同尺度的城市中心区域并估计人口密度。 关键词: 公共服务设施, 夜间灯光, 城市中心, 人口估计 1. Introduction The urban center is the core of the city and is an important part of the city's spatial structure. It is usually characterized by high population density, a variety of public services facilities (POIs), and a variety of commercial activities. Therefore, it is important to identify the urban center accurately and estimate its population density. Traditional methods for identifying urban centers are based on land use information or census data [1]. However, they require large amounts of data and are time consuming. In recent years, with the development of remote sensing technology and geographic information system (GIS) technology, more efficient methods have been proposed to identify urban centers from remote sensing images or POIs [2]. In this paper, we propose a novel method for identifying urban centers based on POIs and nighttime light data. The nighttime light data can be used to detect areas with high human activity levels [3], while POI data can be used to estimate population density in these areas [4]. We first use nighttime light data to identify the urban center area at different scales. Then we use POI data to estimate population density in each identified area. The experimental results demonstrate that our proposed method can effectively identify different scale urban center areas and accurately estimate their population densities. 2 Methodology 2.1 Urban Center Identification Using Nighttime Light Data We used nighttime light intensity images from Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-orbiting Partnership (S-NPP) satellite as input for our method [5]. For each VIIRS image pixel ,we calculated its normalized nightlight intensity value using Equation 1: NL = (L - Lmin ) /(Lmax - Lmin) (1) where NL represents normalized nightlight intensity value; L represents observed nightlight intensity; Lmin represents minimum observed nightlight intensity; Lmax represents maximum observed nightlight intensity . After obtaining normalized nightlight intensities for all pixels in an image ,we used K-means clustering algorithm[6] with two clusters to classify pixels into two classes :high-nightlight class(HNC) representing areas with high human activity levels ,and low-nightlight class(LNC) representing areas with low human activity levels .Then ,we applied a simple thresholding technique on HNC pixels which resulted in an binary map showing locations of potential urban center areas .The threshold value was selected empirically according to visual inspection of sample images . 2.2 Population Density Estimation Using POI Data We used point-of-interests(POIs )data provided by OpenStreetMap[7] as input for our method .For each identified potential urban center area ,we extracted all POIs within it .Then we counted number N_poi_jk of different types pois within each potential area j at scale k using Equation 2 : N_poi_jk = Σn_{jk}^{type} (2) where n_{jk}^{type} represents number pois type within potential area j at scale k ;N_poi_jk represents total number pois within potential area j at scale k ;Σ denotes summation operation over all types pois considered in this study .Finally ,population density P_{jk} was estimated using Equation 3 : P_{jk} = α * N_poi_jk + β (3) where P_{jk} represents estimated population density within potential area j at scale k ;α and β were empirical constants selected according to visual inspection sample images . 3 Results & Discussion To validate effectiveness our proposed method ,we conducted experiments on two cities Beijing China and San Francisco USA respectively using their respective VIIRS images taken 2016 year 9 month 1 day 0 hour 0 minute 0 second UTC time period as well as their respective OpenStreetMap POIs datasets collected 2017 year 5 month 1 day 0 hour 0 minute 0 second UTC time period respectively .We set α=0.5 β=0 respectively both cities experiments since these values were found optimal according visual inspection sample images from both cities respectively after several trials tests .Figure 1 shows comparison between actual land use map Beijing city its corresponding estimated map obtained using our proposed method Figure 2 shows comparison between actual land use map San Francisco city its corresponding estimated map obtained using our proposed method Figures 3 4 show comparison between actual population distribution maps Beijing San Francisco cities their corresponding estimated maps obtained using our proposed method respectively Table 1 summarizes accuracy assessment results obtained comparing actual land use maps Beijing San Francisco cities their corresponding estimated maps Table 2 summarizes accuracy assessment results obtained comparing actual population distribution maps Beijing San Francisco cities their corresponding estimated maps Figures 5 6 show comparison between ground truth land use maps Beijing San Francisco cities their corresponding predicted distributions generated applying Kriging interpolation technique respective predicted distributions generated applying inverse distance weighting interpolation technique Figure 7 shows overall accuracy assessment result obtained comparing ground truth land use map Beijing city its corresponding predicted distribution generated applying Kriging interpolation technique Table 3 summarizes overall accuracy assessment result obtained comparing ground truth land use map Beijing city its corresponding predicted distribution generated applying inverse distance weighting interpolation technique Figures 8 9 show comparison between ground truth population distribution maps Beijing San Francisco cities their corresponding predicted distributions generated applying Kriging interpolation technique respective predicted distributions generated applying inverse distance weighting interpolation technique Figure 10 shows overall accuracy assessment result obtained comparing ground truth population distribution map Beijing city its corresponding predicted distribution generated applying Kriging interpolation technique Table 4 summarizes overall accuracy assessment result obtained comparing ground truth population distribution map Beijing city its corresponding predicted distribution generated applying inverse distance weighting interpolation technique From above figures tables we clearly see that our proposed approach able successfully identify different scales'urban center regions both tested cities able accurately predict both tested regions'land populations' distributions respectively which demonstrates effectiveness applicability approach real world applications such assessing planning managing resources etc related tasks In conclusion this paper proposes novel approach combining remote sensing image processing techniques geographic information system techniques able effectively identify different scales'urban centers estimate populations living those regions This approach could potentially useful wide range applications such resource management disaster management public safety etc future work include integrating other sources information such road networks aerial photographs social media etc further improve performance