Objectives Digital media and sensing technologies have created new ways to present maps. WeMaps have grown quickly because they are easy to make and easy to share. They use microcontent and offer only what most users need. Many people now rely on them in indoor and outdoor settings. Yet the support for wayfinding remains weak. Direction cues are often vague and distance cues are often absent. Users with a weak sense of direction struggle even more. Existing landmark or shortest-path methods offer limited help. They focus on path choice or landmark weights and ignore key spatial relations. Some of them return the shortest route but give no idea of how to move along it. Others highlight landmarks but fail to guide users between them. Traditional direction models add long lists of direction types. These details are too complex for fast decisions. They also break the "just enough and no extra" idea behind microcontent. A direction‐distance model (DDM) is introduced to fill this gap and to give clear, compact, and non-redundant guidance. The goal is to supply essential spatial cues without adding noise. The design also considers indoor scenarios such as large stations, malls, and parking garages, where common mobile map apps often fail to give accurate or readable direction hints. The model aims to support these cases as well.

Methods The core features of WeMaps are examined to find what matters most during wayfinding. Direction and distance emerge as the essential elements. They also show clear advantages over landmark-based cues. Both features can be presented with a small amount of information yet still guide users through complex spaces. Reasons for the failure of classic direction models are analyzed that many existing models add direction types that users do not need. Such extra information slows decision-making. Some methods also rebuild the direction model every time the user moves. This adds delay and causes outdated cues. The DDM is built in two parts. First, path azimuths are calculated to give simple numeric direction values. These values are then grouped with an eight‑direction cone model. The grouping reduces noise and keeps the output short. The same rule applies to long routes, curved routes, and routes with multiple turns. Paths with and without shape points are handled separately to avoid errors caused by dense geometry. Second, distance is computed with Euclidean metrics. Segment functions and road-section functions are defined and summed to give total distance. Both local and global distance cues are tested. The output includes step‑level distances and overall route length. Experiments use road‑network data from different map types and several indoor layouts are added to test short segments and complex turn sequences. Each dataset produces direction output, distance output, and combined descriptions. The results are compared with current direction and distance models, including shortest‑path methods and landmark‑weight methods. The comparison focuses on time cost, clarity, and redundancy.

Results The DDM removes repeated modeling steps that many direction models require. It reduces time cost and supports fast guidance during wayfinding. The improvement is clear on paths with many turns. Direction relations are expressed in both numeric and verbal form with little noise. The output stays short even for long or irregular paths. The model also adds missing direction and distance cues that WeMaps often lack. Users with weak direction sense can gain clearer support. The combined direction‑distance output gives a more natural description for real users. It shows the next turn, the direction of movement, and the approximate distance. It avoids long lists of direction types and landmark overload. Compared with existing spatial direction models, DDM keeps stable performance when the geometry changes. Compared with distance‑only models, DDM preserves the sense of movement that users need to maintain orientation. The model also avoids lag from repeated reconstruction, which helps in dynamic settings.

Conclusions The DDM fits the microcontent nature of WeMaps and adds key information for wayfinding. It overcomes the limits of landmark‑based and traditional direction or distance methods. The output avoids extra details and supports quick decisions. It offers simple direction steps and clear distance cues in one compact form. Some direction descriptions for curved paths remain complex. Further work will simplify these descriptions and improve how users read and remember them. Work will also explore adaptive rules that adjust the amount of information based on the user's directional sense and the layout of the space.