Abstract
Functional areas are the basic spatial units in which cities or development zones implement urban plans and provide functions. Internet map big data technology provides a new method for the identification and spatial analysis of functional areas. Based on the POI (point of interest) data from AMap (a map application of AutoNavi) from 2017, this paper proposes an urban functional areas recognition and analysis method based on the frequency density and the ratio of POI function types. It takes the Guangzhou Economic and Technological Development Zone as a case study to analyze the main function and spatial distribution characteristics of the detailed functional areas. The research shows the following: (1) The POI frequency density index and the function type ratio can effectively distinguish the functions of the grid units and analyze the spatial distribution characteristics of a complex functional area. (2) The single functional area is the most common area type in the Guangzhou Economic and Technological Development Zone. The largest proportion of all areas is allocated to traditional manufacturing industry functional areas, followed by high-tech enterprises, catering and entertainment, real estate, and education and health care, in descending order. The smallest proportion is allocated to finance and insurance functional areas. (3) The current layout of the functional areas in the Guangzhou Economic and Technological Development Zone conforms to the overall requirements and planning objectives of the central and local government. The layout and agglomeration of different blocks within the economic development zone are consistent with local industry’s target orientation and development history.
Highlights
The urban functional areas are geographic units composed of various interrelated elements within a city [1,2,3]
On the 250 m scale, single functional areas accoun6 toffo15r 52.46% of the total area of the development zone, mixed functional areas account for 17.07%, and no tdraatdaitaiorenaasl amccaonuunftacfoturr3in0.g46i%nd. uSsptartyiaallnyd, thhiegYh-otnecghheenDtiesrtprircitseanfdunthcteioEnaasltearrneaDsisatrreicctoanrecednotrmaitnedateind YboynsginhgelDe ifsutnriccttioannadl Earaesatse,rnwDhiilsetrtihcet bWuetsaterernspRaergsieoinnitshcehWareasctteerrnizeDdisbtryicsti.nOgltehefurntyctpioens aolfaarreeaassaanrde smcaixtteedrefdunthctriooungahl oauretatsh,ewdiethvethloepmmiexnetdzfounnec.tion areas being slightly larger than the single functional areas
The mixed functional areas of real estate, traditional manufacturing, and high‐tech enterprises are mainly distributed in the south of the Western District, while the mixed Sfuusntacitniaobniliatyl 2a0r1e9a,s11o,f13fi8n5ance and insurance, education and health care, and catering and entertain7moefn15t are mainly distributed in the southwest of the Eastern District
Summary
The urban functional areas are geographic units composed of various interrelated elements within a city [1,2,3]. Based on POI data, Xu Zening et al proposed the DensiGraph analysis method to identify the boundaries of urban built-up areas [35]. Yuan Jing et al used Beijing taxi trajectory data to characterize population flow with the assistance of POI data They proposed a framework named DRoF that discovers regions with different functions to identify Beijing’s functional areas [39]. Previous studies illustrate that we can identify and zone the urban and rural borders, logistics patterns, or urban functional areas by using POI data [40,41]. Development Zone is about 33.35 km, with its GDP (Gross Domestic Product) reaching 320.953 location and sbciollpioen oyfuathn eins2t0u1d7.yTaheresapeacrifeicslhocoawtionn ianndFsigcoupreeo1f .thTehsteuddyisatrreiactasreasrheoYwonninghEreroDr!isRterfiecrte,nEcaestern District, and Western sDouisrcterincot,t fforounmd..tTohpe tdoistbriocttstoarme YionngthheeDrisigtrhict, oEafsFteirgnuDriestr1ic,t,wanitdhWaenstearrneDaisotrfic1t,1f.r4o5mktomp 2, 16.53 km, and 5.37 km2,torebsopttoemctiivnethlye.right of Figure 1, with an area of 11.45 km2, 16.53 km , and 5.37 km, respectively
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