People-oriented urban planning requires that service facilities should efficiently meet individual and community activity needs across the demographic landscape that defines a city. To develop a conceptual basis for urban spatial infrastructure optimization, we empirically studied existing population activities and service facilities in Changchun, China, using kernel density estimation, bivariate spatial autocorrelation analysis, and other models. The spatial relationships we derived from multiple sources of big data such as mobile phone signaling and POI data indicated that the intensity of population activity has obvious temporal regularity, and its spatial distribution is “center-periphery.” Service facilities display a “One main and two subs” distribution with no obvious spatial dependence between the core’s density and diversity. Population activities and service facility diversity show a high-high spatial correlation and multiple matching patterns. At the same time, a certain degree of spatial mismatch between different age groups and service facilities was also observed. Our research suggests several urban renewal actions to rectify this mismatch, such as: decentralizing the core area medical service facilities; reducing the attractiveness of the core area and its traffic pressure; and renewing and renovating old facilities to reduce construction costs. At a government planning level, construction along the periphery of the urban can enrich the diversity of its service facilities to improve the efficiency of spatial allocation.
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