Relevant bibliographies by topics / Spatially explicit

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  1. Journal articles

Academic literature on the topic 'Spatially explicit'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 June 2025

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Journal articles on the topic "Spatially explicit"

1

Pulliam, H. Ronald, and John B. Dunning. "Spatially Explicit Population Models." Ecological Applications 5, no. 1 (1995): 2. http://dx.doi.org/10.2307/1942044.

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2

Lutscher, Frithjof, and Mark A. Lewis. "Spatially-explicit matrix models." Journal of Mathematical Biology 48, no. 3 (2004): 293–324. http://dx.doi.org/10.1007/s00285-003-0234-6.

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3

Nijssen, David, and Andreas H. Schumann. "Aggregating spatially explicit criteria: avoiding spatial compensation." International Journal of River Basin Management 12, no. 1 (2014): 87–98. http://dx.doi.org/10.1080/15715124.2014.882845.

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4

Allen, J. C., C. C. Brewster, and D. H. Slone. "Spatially explicit ecological models: a spatial convolution approach." Chaos, Solitons & Fractals 12, no. 2 (2001): 333–47. http://dx.doi.org/10.1016/s0960-0779(00)00092-8.

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5

CARPENTER, T. E. "Stochastic, spatially-explicit epidemic models." Revue Scientifique et Technique de l'OIE 30, no. 2 (2011): 417–24. http://dx.doi.org/10.20506/rst.30.2.2044.

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6

Chandler, Richard B., and Joseph D. Clark. "Spatially explicit integrated population models." Methods in Ecology and Evolution 5, no. 12 (2014): 1351–60. http://dx.doi.org/10.1111/2041-210x.12153.

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7

Lamb, Eric G., Kerrie L. Mengersen, Katherine J. Stewart, Udayanga Attanayake, and Steven D. Siciliano. "Spatially explicit structural equation modeling." Ecology 95, no. 9 (2014): 2434–42. http://dx.doi.org/10.1890/13-1997.1.

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8

Sagebiel, Julian, Klaus Glenk, and Jürgen Meyerhoff. "Spatially explicit demand for afforestation." Forest Policy and Economics 78 (May 2017): 190–99. http://dx.doi.org/10.1016/j.forpol.2017.01.021.

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9

Yang, Jun, Xiangyu Luo, Yixiong Xiao, et al. "Comparing the Use of Spatially Explicit Indicators and Conventional Indicators in the Evaluation of Healthy Cities: A Case Study in Shenzhen, China." International Journal of Environmental Research and Public Health 17, no. 20 (2020): 7409. http://dx.doi.org/10.3390/ijerph17207409.

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Abstract:
Various indicator systems have been developed to monitor and assess healthy cities. However, few of them contain spatially explicit indicators. In this study, we assessed four health determinants in Shenzhen, China, using both indicators commonly included in healthy city indicator systems and spatially explicit indicators. The spatially explicit indicators were developed using detailed building information or social media data. Our results showed that the evaluation results of districts and sub-districts in Shenzhen based on spatially explicit indicators could be positively, negatively, or not associated with the evaluation results based on conventional indicators. The discrepancy may be caused by the different information contained in the two types of indicators. The spatially explicit indicators measure the quantity of the determinants and the spatial accessibility of these determinants, while the conventional indicators only measure the quantity. Our results also showed that social media data have great potential to represent the high-resolution population distribution required to estimate spatially explicit indicators. Based on our findings, we recommend that spatially explicit indicators should be included in healthy city indicator systems to allow for a more comprehensive assessment of healthy cities.
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10

Müller, Daniel. "Stata in Space: Econometric Analysis of Spatially Explicit Raster Data." Stata Journal: Promoting communications on statistics and Stata 5, no. 2 (2005): 224–38. http://dx.doi.org/10.1177/1536867x0500500207.

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Abstract:
Realizing the importance of location, economists are increasingly adopting spatial analytical and spatial econometric perspectives to study questions such as the geographical targeting of policy interventions, regional agglomeration effects, the diffusion of technologies across space, or causes and consequences of land-cover change. Explicitly accounting for location in econometric estimations can be of great benefit for researchers working at the interface of economics or environmental sciences and geography. The objective of this article is to demonstrate how spatially explicit raster data derived from standard geographical information system (GIS) software can be used within Stata. Three programs implemented as ado-files are presented. These import geographic raster data into Stata (ras2dta), draw systematic spatial samples within Stata (spatsam), and export data and estimation results in a form usable by standard GIS software (dta2ras). A numerical example is presented to estimate the determinants of forest cover with a spatially explicit logit model, calculate predicted probabilities, and map the predictions with GIS software.
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