CostMAP: an open-source software package for developing cost surfaces using a multi-scale search kernel

Abstract

Cost Surfaces are a quantitative means of assigning social, environmental, and engineering costs that impact movement across landscapes. Cost surfaces are a crucial aspect of route optimization and least cost path (LCP) calculations and are used in a wide range of disciplines including computer science, landscape ecology, and energy infrastructure modeling. Linear features present a key weakness to traditional routing calculations along costs surfaces because they cannot identify whether moving from a cell to its adjacent neighbors constitutes crossing a linear barrier (increased cost) or following a corridor (reduced cost). Following and avoiding linear features can drastically change predicted routes. In this paper, we introduce an approach to address this adjacency issue using a search kernel that identifies these critical barriers and corridors. We have built this approach into a new Java-based open-source software package-CostMAP (cost surface multilayer aggregation program)-which calculates cost surfaces and cost networks using the search kernel. CostMAP not only includes the new "adjacency" capability, it is also a versatile multi-platform package that allows users to input multiple GIS data layers and to set weights and rules for developing a weighted-cost network. We compare CostMAP performance with traditional cost surface approaches and show significant performance gains-both following corridors and avoiding barriers-using examples in a movement ecology framework and pipeline routing for carbon capture, and storage (CCS). We also demonstrate that the new software can straightforwardly calculate cost surfaces on a national scale.

Cost distances and least cost paths respond differently to cost scenario variations: a sensitivity analysis of ecological connectivity modeling

“…As this thematic resolution did not reflect the simplified land cover maps commonly used for connectivity modeling and would not have allowed a fine assessment of the influence of the cost assigned to each land cover type, we reclassified it into five land cover types: (1) forests, (2) grasslands and woody perennial crops (grasslands hereafter), (3) annual crops, (4) artificial areas (built-up land, roads and transport infrastructures), and ( 5) others (water and other land cover types). The spatial and thematic resolutions of this raster layer allowed us to correctly account for the barrier effects of linear landscape features such as transport infrastructures, which can largely influence LCP modeling (Hoover et al, 2020).…”

The Importance of Modeling Carbon Dioxide Transportation and Geologic Storage in Energy System Planning Tools

“…To do this, SCO 2 T uses reduced-order models that replicate full-physics dynamic reservoir simulations (Chen et al, 2020). We modify SCO 2 T by 1) adding all site-level costs from the Environmental Protection Agency (EPA) geologic CO 2 storage cost model (Environmental Protection Agency, 2010); 2) adding an Excel MACRO to generate supply curves; and 3) removing areas from the SCO 2 T subsurface dataset that prior work suggests cannot be developed for geothermal power plants or geologic CO 2 storage sites (Young et al, 2019;Hoover et al, 2020). Below we list some assumptions made to apply the EPA cost model for SCO 2 T for this study.…”

Research on the Development of CCUS Full Process Technology

“…The implication of CCUS source-sink matching: the CO 2 emission sources in a region are geographically dispersed, with different emissions, and the CO 2 utilization and geological storage sinks have different carbon removal potentials and methods. CO 2 transport modes and transport costs between sources and sinks are affected by factors such as regional geographical conditions, land use types, rivers, traffic and crowd density [6]. It can reduce CO 2 transport costs and maximize CO 2 utilization and geological storage by constructing the source-sink matching relationship of CCUS scientifically.…”