Journal articles: '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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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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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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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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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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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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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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11

Lanchier, N., and C. Neuhauser. "Spatially explicit non-Mendelian diploid model." Annals of Applied Probability 19, no. 5 (2009): 1880–920. http://dx.doi.org/10.1214/09-aap598.

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12

South, Andy. "Dispersal in Spatially Explicit Population Models." Conservation Biology 13, no. 5 (1999): 1039–46. http://dx.doi.org/10.1046/j.1523-1739.1999.98236.x.

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13

Veldtman, Ruan, and Melodie A. McGeoch. "Spatially explicit analyses unveil density dependence." Proceedings of the Royal Society of London. Series B: Biological Sciences 271, no. 1556 (2004): 2439–44. http://dx.doi.org/10.1098/rspb.2004.2905.

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14

Minor, E. S., R. I. McDonald, E. A. Treml, and D. L. Urban. "Uncertainty in spatially explicit population models." Biological Conservation 141, no. 4 (2008): 956–70. http://dx.doi.org/10.1016/j.biocon.2007.12.032.

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15

Zhao, Qing, J. Andrew Royle, and G. Scott Boomer. "Spatially explicit dynamic N-mixture models." Population Ecology 59, no. 4 (2017): 293–300. http://dx.doi.org/10.1007/s10144-017-0600-7.

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16

Liu, Yan Y., and Wendy K. Tam Cho. "A spatially explicit evolutionary algorithm for the spatial partitioning problem." Applied Soft Computing 90 (May 2020): 106129. http://dx.doi.org/10.1016/j.asoc.2020.106129.

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17

Brandt, Stephen B., Doran M. Mason, and E. Vincent Patrick. "Spatially-explicit Models of Fish Growth Rate." Fisheries 17, no. 2 (1992): 23–35. http://dx.doi.org/10.1577/1548-8446(1992)017<0023:smofgr>2.0.co;2.

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18

ROGERS, DAVID J., and LUIGI SEDDA. "Statistical models for spatially explicit biological data." Parasitology 139, no. 14 (2012): 1852–69. http://dx.doi.org/10.1017/s0031182012001345.

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SUMMARYExisting algorithms for predicting species' distributions sit on a continuum between purely statistical and purely biological approaches. Most of the existing algorithms are aspatial because they do not consider the spatial context, the occurrence of the species or conditions conducive to the species' existence, in neighbouring areas. The geostatistical techniques of kriging and cokriging are presented in an attempt to encourage biologists more frequently to consider them. Unlike deterministic spatial techniques they provide estimates of prediction errors. The assumptions and applications of common geostatistical techniques are presented with worked examples drawn from a dataset of the bluetongue outbreak in northwest Europe in 2006. Emphasis is placed on the importance and interpretation of weights in geostatistical calculations. Covarying environmental data may be used to improve predictions of species’ distributions, but only if their sampling frequency is greater than that of the species’ or disease data. Cokriging techniques are unable to determine the biological significance or importance of such environmental data, because they are not designed to do so.

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19

Bertuzzo, E., R. Casagrandi, M. Gatto, I. Rodriguez-Iturbe, and A. Rinaldo. "On spatially explicit models of cholera epidemics." Journal of The Royal Society Interface 7, no. 43 (2009): 321–33. http://dx.doi.org/10.1098/rsif.2009.0204.

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We generalize a recently proposed model for cholera epidemics that accounts for local communities of susceptibles and infectives in a spatially explicit arrangement of nodes linked by networks having different topologies. The vehicle of infection ( Vibrio cholerae ) is transported through the network links that are thought of as hydrological connections among susceptible communities. The mathematical tools used are borrowed from general schemes of reactive transport on river networks acting as the environmental matrix for the circulation and mixing of waterborne pathogens. Using the diffusion approximation, we analytically derive the speed of propagation for travelling fronts of epidemics on regular lattices (either one-dimensional or two-dimensional) endowed with uniform population density. Power laws are found that relate the propagation speed to the diffusion coefficient and the basic reproduction number. We numerically obtain the related, slower speed of epidemic spreading for more complex, yet realistic river structures such as Peano networks and optimal channel networks. The analysis of the limit case of uniformly distributed population sizes proves instrumental in establishing the overall conditions for the relevance of spatially explicit models. To that extent, the ratio between spreading and disease outbreak time scales proves the crucial parameter. The relevance of our results lies in the major differences potentially arising between the predictions of spatially explicit models and traditional compartmental models of the susceptible–infected–recovered (SIR)-like type. Our results suggest that in many cases of real-life epidemiological interest, time scales of disease dynamics may trigger outbreaks that significantly depart from the predictions of compartmental models.

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20

Burnett, James Wesley, and Xueting Zhao. "Spatially Explicit Prediction of Wholesale Electricity Prices." International Regional Science Review 40, no. 2 (2016): 99–140. http://dx.doi.org/10.1177/0160017615607055.

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Transmission constraints often limit the flow of electricity in a regional transmission network leading to strong interaction effects across different geographically distributed points within the system. In modern wholesale electricity markets, these transmission constraints lead to spatial patterns within the nodal electricity spot prices. This study exploits these spatial patterns to better predict spot prices within a wholesale electricity market. More specifically, we use the latest spatial panel data econometric models to compare within-sample and out-of-sample forecasts against nonspatial panel data models. The spatial panel data approach is explained by demonstrating a simple network optimization model. We find that a dynamic, spatial panel data model provides the best predictions within a forecasting error context. Our results may suggest that the spatial autocorrelation between node prices extends beyond the current market-defined zonal boundaries, which calls into question whether the zonal boundaries accurately reflect the congestion boundaries within the system.

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21

Walker, Robert. "Evaluating the Performance of Spatially Explicit Models." Photogrammetric Engineering & Remote Sensing 69, no. 11 (2003): 1271–78. http://dx.doi.org/10.14358/pers.69.11.1271.

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22

Wei, Ran, Sergio Rey, and Elijah Knaap. "Efficient regionalization for spatially explicit neighborhood delineation." International Journal of Geographical Information Science 35, no. 1 (2020): 135–51. http://dx.doi.org/10.1080/13658816.2020.1759806.

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23

Kunapo, Joshphar, Tim D. Fletcher, Anthony R. Ladson, Luke Cunningham, and Matthew J. Burns. "A spatially explicit framework for climate adaptation." Urban Water Journal 15, no. 2 (2018): 159–66. http://dx.doi.org/10.1080/1573062x.2018.1424216.

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24

Gatto, Marino, Lorenzo Mari, Enrico Bertuzzo, et al. "Spatially Explicit Conditions for Waterborne Pathogen Invasion." American Naturalist 182, no. 3 (2013): 328–46. http://dx.doi.org/10.1086/671258.

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25

Bacaro, G., and C. Ricotta. "A spatially explicit measure of beta diversity." Community Ecology 8, no. 1 (2007): 41–46. http://dx.doi.org/10.1556/comec.8.2007.1.6.

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26

Glass, Kevin, Marilynn Livingston, and John Conery. "Distributed simulation of spatially explicit ecological models." ACM SIGSIM Simulation Digest 27, no. 1 (1997): 60–63. http://dx.doi.org/10.1145/268823.268902.

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27

Beelman, Ewa, and Boleslaw K. Szymanski. "Breadth-first rollback in spatially explicit simulations." ACM SIGSIM Simulation Digest 27, no. 1 (1997): 124–31. http://dx.doi.org/10.1145/268823.268910.

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28

Mooij, Wolf M., and Donald L. DeAngelis. "UNCERTAINTY IN SPATIALLY EXPLICIT ANIMAL DISPERSAL MODELS." Ecological Applications 13, no. 3 (2003): 794–805. http://dx.doi.org/10.1890/1051-0761(2003)013[0794:uisead]2.0.co;2.

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29

Crespo, Ricardo, and Adrienne Grêt-Regamey. "Spatially explicit inverse modeling for urban planning." Applied Geography 34 (May 2012): 47–56. http://dx.doi.org/10.1016/j.apgeog.2011.10.009.

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30

DeAngelis, Donald L., and Simeon Yurek. "Spatially Explicit Modeling in Ecology: A Review." Ecosystems 20, no. 2 (2016): 284–300. http://dx.doi.org/10.1007/s10021-016-0066-z.

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31

Gawecka, Klementyna A., and Jordi Bascompte. "Habitat restoration in spatially explicit metacommunity models." Journal of Animal Ecology 90, no. 5 (2021): 1239–51. http://dx.doi.org/10.1111/1365-2656.13450.

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32

SEMBOLONI, FERDINANDO. "FROM SPATIALLY EXPLICIT TO MULTIAGENTS SIMULATION OF URBAN DYNAMIC." Advances in Complex Systems 10, supp02 (2007): 355–62. http://dx.doi.org/10.1142/s0219525907001409.

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In the introduction a distinction is made between the macroscopic approach, which the spatially explicit method is connected with, and the microscopic approach. The spatially explicit method is considered in relation to the interactions between located variables: without and with transport of matter. It is compared with the microscopic approach and the main difference is highlighted: the long range interactions of the latter instead of the neighborhood-based interactions of the former. Finally, a comprehensive approach based on agents, goods and markets, considered as main classes, is proposed. In this approach, the basic actions — buy, produce and sell — are able to connect the main classes and to generate an economic system and an urban spatial pattern.

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33

Dev, Ashwani, and George A. McMechan. "Spatial antialias filtering in the slowness-frequency domain." GEOPHYSICS 74, no. 2 (2009): V35—V42. http://dx.doi.org/10.1190/1.3052115.

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A rigorous, explicit spatial antialias filter is designed and applied to spatially coarsely sampled seismic data by removing all energy above the first Nyquist wavenumber, and aliased energy that is folded back across the Nyquist, in the horizontal slowness-frequency domain. The spatial filtering in the slowness-frequency domain is explicit, free from any event linearity assumption, and does not require any interpolation. The spatially aliased energy is dispersive, and present at small and large slownesses. Comparison of the output data after antialias spatial filtering, with output data after conventional antialias frequency filtering, shows that the filter removes the spatially aliased frequencies selectively at each slowness; antialias low-pass frequency filtering under- or overcorrects for spatial aliasing at all slownesses. A seismic gather can be spatially dealiased only at the expense of wavelet spectral changes; dealiasing and preservation of amplitude variations with offset are not simultaneously possible.

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34

HUI, CANG, MELODIE A. McGEOCH, and MARIE WARREN. "A spatially explicit approach to estimating species occupancy and spatial correlation." Journal of Animal Ecology 75, no. 1 (2006): 140–47. http://dx.doi.org/10.1111/j.1365-2656.2005.01029.x.

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35

Massimino, Dario, Alison Johnston, David G. Noble, and James W. Pearce-Higgins. "Multi-species spatially-explicit indicators reveal spatially structured trends in bird communities." Ecological Indicators 58 (November 2015): 277–85. http://dx.doi.org/10.1016/j.ecolind.2015.06.001.

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36

Chen, Min, and Qianlai Zhuang. "Spatially Explicit Parameterization of a Terrestrial Ecosystem Model and Its Application to the Quantification of Carbon Dynamics of Forest Ecosystems in the Conterminous United States." Earth Interactions 16, no. 5 (2012): 1–22. http://dx.doi.org/10.1175/2012ei400.1.

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Abstract The authors use a spatially explicit parameterization method and the Terrestrial Ecosystem Model (TEM) to quantify the carbon dynamics of forest ecosystems in the conterminous United States. Six key parameters that govern the rates of carbon and nitrogen dynamics in TEM are selected for calibration. Spatially explicit data for carbon and nitrogen pools and fluxes are used to calibrate the six key parameters to more adequately account for the spatial heterogeneity of ecosystems in estimating regional carbon dynamics. The authors find that a spatially explicit parameterization results in vastly different carbon exchange rates relative to a parameterization conducted for representative ecosystem sites. The new parameterization method estimates that the net ecosystem production (NEP), the annual gross primary production (GPP), and the net primary production (NPP) of the regional forest ecosystems are 61% (0.02 Pg C; 1 Pg = 1015 g) higher and 2% (0.11 Pg C) and 19% (0.45 Pg C) lower, respectively, than the values obtained using the traditional parameterization method for the period 1948–2000. The estimated vegetation carbon and soil organic carbon pool sizes are 51% (18.73 Pg C) lower and 29% (7.40 Pg C) higher. This study suggests that, to more adequately quantify regional carbon dynamics, spatial data for carbon and nitrogen pools and fluxes should be developed and used with the spatially explicit parameterization method.

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37

Steele, Caitriana M., Brandon T. Bestelmeyer, Laura M. Burkett, Philip L. Smith, and Steven Yanoff. "Spatially Explicit Representation of State-and-Transition Models." Rangeland Ecology & Management 65, no. 3 (2012): 213–22. http://dx.doi.org/10.2111/rem-d-11-00047.1.

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38

Moen, Ron, John Pastor, and Yosef Cohen. "A SPATIALLY EXPLICIT MODEL OF MOOSE FORAGINGAND ENERGETICS." Ecology 78, no. 2 (1997): 505–21. http://dx.doi.org/10.1890/0012-9658(1997)078[0505:asemom]2.0.co;2.

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39

Kerkman, Kasper, Karel Martens, and Henk Meurs. "Predicting travel flows with spatially explicit aggregate models." Transportation Research Part A: Policy and Practice 118 (December 2018): 68–88. http://dx.doi.org/10.1016/j.tra.2018.08.029.

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40

Casagrandi, Renato, and Marino Gatto. "The intermediate dispersal principle in spatially explicit metapopulations." Journal of Theoretical Biology 239, no. 1 (2006): 22–32. http://dx.doi.org/10.1016/j.jtbi.2005.07.009.

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41

C. Reluga, Timothy, and Allison K. Shaw. "Optimal migratory behavior in spatially-explicit seasonal environments." Discrete & Continuous Dynamical Systems - B 19, no. 10 (2014): 3359–78. http://dx.doi.org/10.3934/dcdsb.2014.19.3359.

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42

Antonaki, Margarita, and Anna Philippou. "A Process Calculus for Spatially-explicit Ecological Models." Electronic Proceedings in Theoretical Computer Science 100 (November 15, 2012): 14–28. http://dx.doi.org/10.4204/eptcs.100.2.

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43

Efford, Murray G., and Rachel M. Fewster. "Estimating population size by spatially explicit capture-recapture." Oikos 122, no. 6 (2012): 918–28. http://dx.doi.org/10.1111/j.1600-0706.2012.20440.x.

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44

Flaxman, S. M., J. L. Feder, and P. Nosil. "Spatially explicit models of divergence and genome hitchhiking." Journal of Evolutionary Biology 25, no. 12 (2012): 2633–50. http://dx.doi.org/10.1111/jeb.12013.

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45

STRAND, ALLAN E., and JAMES M. NIEHAUS. "kernelpop, a spatially explicit population genetic simulation engine." Molecular Ecology Notes 7, no. 6 (2007): 969–73. http://dx.doi.org/10.1111/j.1471-8286.2007.01832.x.

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46

Wiegand, Thorsten, Eloy Revilla, and Felix Knauer. "Dealing with Uncertainty in Spatially Explicit Population Models." Biodiversity and Conservation 13, no. 1 (2004): 53–78. http://dx.doi.org/10.1023/b:bioc.0000004313.86836.ab.

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47

Lawrence, Tara N., and R. S. Bhalla. "Spatially explicit action research for coastal fisheries management." PLOS ONE 13, no. 7 (2018): e0199841. http://dx.doi.org/10.1371/journal.pone.0199841.

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48

HONEY-ROSÉS, JORDI, KATHY BAYLIS, and M. ISABEL RAMÍREZ. "A Spatially Explicit Estimate of Avoided Forest Loss." Conservation Biology 25, no. 5 (2011): 1032–43. http://dx.doi.org/10.1111/j.1523-1739.2011.01729.x.

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49

Linkov, Igor, Alexander Grebenkov, and Vladimir M. Baitchorov. "Spatially explicit exposure models: application to military sites." Toxicology and Industrial Health 17, no. 5-10 (2001): 230–35. http://dx.doi.org/10.1191/0748233701th116oa.

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50

Russell, Periann P., Susan M. Gale, Breda Muñoz, John R. Dorney, and Matthew J. Rubino. "A Spatially Explicit Model for Mapping Headwater Streams." JAWRA Journal of the American Water Resources Association 51, no. 1 (2014): 226–39. http://dx.doi.org/10.1111/jawr.12250.

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Journal articles on the topic 'Spatially explicit'

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