Traditional evaluation methods predominantly rely on the center of inertia frequency index, which only reflects the overall average frequency dynamics of the network, ignoring the significant characteristics of time-varying inertia and uneven spatial distribution under high penetration of renewable energy. To address the issue of varying system inertia levels caused by large-scale wind power participating in inertia support and frequency regulation, this paper proposes an inertia demand evaluation method for wind power-integrated power systems considering the spatiotemporal characteristics of inertia. Firstly, a system frequency response model incorporating wind power comprehensive control is constructed by integrating wind power virtual inertia response and pitch angle primary frequency regulation control. The system transfer function and the calculation formula for the equivalent inertia time constant are derived, clarifying the support mechanism of wind power in delaying the rate of change of frequency through rapid response to active power disturbances. Secondly, a characterization method considering the spatiotemporal characteristics of inertia is proposed. Based on the analysis of the nodal power-frequency mechanism, a node inertia matrix and a temporal dynamic model are constructed to quantify the temporal evolution laws of inertia at the same node and the spatial distribution differences among different nodes, thereby overcoming the limitations of traditional center of inertia frequency evaluation. Furthermore, by obtaining the system power-frequency equation and frequency response model, an inertia time constant model is constructed to form an inertia demand evaluation model considering spatiotemporal characteristics. Finally, simulation verification is conducted based on the modified IEEE 10-machine 39-bus system. The results demonstrate that the participation of wind power can effectively enhance the system's inertia support capability. The proposed method accurately captures inertia differences across different nodes at different times and realizes the visualization of spatiotemporal characteristics through inertia heat maps, providing a quantitative basis for inertia configuration in the planning stage and frequency stability control in the operation stage of new power systems.