Wildlife crossing structures (WCS) are often proposed as the way to improve wildlife connectivity across transportation and other infrastructure. Barrier fencing is used independently or tied to WCS to reduce wildlife-vehicle collisions (WVC). Effectiveness of WCS and fencing depends on understanding the influences of wildlife needs, topographic constraints, human development and activity, land ownership and management, and other variables. There are no published, science-based, and objective methods for decision-support for WCS and fencing at US state extents. This is a logical task for the field of geographic information systems, which originated from a need to digitally represent and analyze natural and land-use features to spatially-inform decisions. I present here a transparent, repeatable method to include consideration of important factors for WCS planning in a GIS, including: 1) where are new over OR under-crossings and fencing ecologically needed and feasible to construct, and 2) where do existing structures provide connectivity? I used 8 highways in CA as case studies:  2 state highways (SR84, SR152), 2 US routes (US101, US395), and 4 interstates (I5, I8, I580, and I680). These highways are distributed throughout CA and impact wildlife connectivity. Planning for WCS and fencing for all locations was supported by stakeholder processes, multiple state and local agencies, and funding support from the CA Wildlife Conservation Board (total support = $28 million). I used the following spatial datasets: 1) available wildlife occurrences (e.g., from camera traps), 2) WVC, 3) collared animal movement, 4) habitat suitability models, 5) topography, 6) human disturbance and development, 7) ownership, and 8) infrastructure data. I developed a logic-based model in GIS to associate the data in a decision-support framework. This “spatially-explicit decision support” (SEDS) approach was based in an extensive literature on GIS and decision-support systems and is useful in the context of incomplete and sometimes conflicting information and needs. The model rules were based on a combination of binary and continuous use of variables. I used the model to 1) identify stretches of highway where fencing could cost-effectively reduce collisions; 2) highlight stretches of highway where WCS were practical and could resolve connectivity; and 3) identify existing structures that could play an important role in wildlife movement. The result of the modeling was a geographically-explicit identification of places along each highway of particular types of actions critical to reduce wildlife-vehicle conflict and improve connectivity. The modeling approach could be used for other similar types of WCS decision-making, at multiple scales. For each highway, one to three locations are being or have been evaluated by engineers and plans developed for construction of 13 WCS and fence alignments, with a combined eventual cost of over $200 million. In every case, additional consideration is being given to enhancing existing bridges and culverts to improve wildlife passage.