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Coin flip questions made easy. Total no. Her average after all. Kinetic energy d. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods.
Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates.
As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables.
CE Database subject headings: Estimation, Geostatistics, Construction costs, Planning, Budgeting Introduction In projects, success is achieved when, under a specified budget within a scheduled duration, predefined quality requirements are met Duncan, In construction, cost estimating is the determination of quantities and the prediction of the costs required to construct and commission a facility within a defined scope.
However, these preliminary cost estimates are crucial to decision-making during early phases of a project with accuracy of preliminary cost estimation methods being a primary determinant to a project's success. In practice, the most typical preliminary estimating procedure is the stochastic method. This method mainly utilizes adjustment factors to derive the cost estimating relationships among the independent variables.
The use of these adjustment factors allows obtain a quick order-of-magnitude estimate of the facility cost by simply adjusting historically based estimates by time, project location and size Remer et al.
When adjusting for project location, various datasets provide location cost adjustment factors LCAFs for sample locations in North America, but, obviously, not all locations are included. To adjust estimates for un-sampled locations, the construction industry has adopted a simple, proximity-based interpolation method, the Nearest Neighbor NN method, which considers only the shortest linear-distance between two sites.
In the CNN method, the values of un-sampled locations are assumed equal to the LCAF value of the closest location within the same state as the project location. Spatial interpolation methods, such as the NN and CNN methods, utilize a set algorithm that estimates values for un-sampled locations by interpolation of sampled LCAF values based on historical construction costs in surrounding locations.
However, sampled cost adjustment factor values can be affected by many other site factors, such as population, households, and income at the sampled location. Simple interpolation would ignore the effect of these values.
On the contrary, spatial prediction methods use statistical analysis to examine or quantify the relationships among a set of independent variables covariates and the available LCAF values. The information obtained from that analysis can then be used to estimate the LCAF values for un-sampled locations. The objective of this paper is to evaluate and assess the accuracy of various spatial prediction methods in estimating the value of LCAFs for un-sampled locations with respect to spatial interpolation methods.
In addition, we attempt to determine if the observed variability in LCAFs could be partially explained by independent socio-economic variables. The presence of local spatial variability is commonly ascribed in the geo-statistical literature to a pattern of factors.
Besides the mere effect of spatial proximity so that the local costs may be influenced by the general costs in a given area , the spatial patterns of the observed construction costs may be explained by a set of unknown and sometimes unobserved local covariates.
The rationale is that, once the available variables are taken into account in the analysis, the performance of the regression-based methods should be equivalent or better than that of the existing proximity-based methods. Instead, if the regression methods are not able to capture the observed variability appropriately, we could be inclined to conclude that either mere proximity plays an important role in costs' determination or there are other variables yet not considered in the analysis that may be important to consider.
Literature Review Location cost adjustment factors Price variability of equipment, plants, or buildings has a temporal component depending on the competitive market conditions and the general state of inflation or deflation of a country's currency, and a geographical component within and between countries.