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GEOSTATS: SUM: assessing socioeconomic factors influence in deforestati

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  • jeanfmas@balamku.uacam.mx
    THE QUESTION WAS: We are studying deforestation in the State of Campeche (South east of Mexico). Overlaying two forest maps of different dates, we generated a
    Message 1 of 2 , Feb 13, 1997
      THE QUESTION WAS:

      We are studying deforestation in the State of Campeche (South east of
      Mexico). Overlaying two forest maps of different dates, we generated
      a digital map which represents the evolution of forest areas. Classes
      of this digital map are non forested areas, unchanged forest areas
      and deforested areas. As a following step, we tried to find out the
      relationship between deforestation and environmental and
      socioeconomic factors such as distance from settlements and
      characteristics of the population.

      Socioeconomic data we used are organized by localities (data are
      given for each settlement), the problem is how to deal with both
      point data and surface data within a GIS. In order to represent the
      influence of a settlement on its environment we developed a simple
      model which is based on two simplifying assumptions: the influence of
      a settlement a) depends on its characteristics and b) decreases with
      the distance from it.

      In order to develop the model, localization of each settlements was
      captured in a point cover, then the settlements point cover was
      converted to a grid using. The value assigned to
      the grid cells was the value of a selected property (for example
      number of people of the settlement) according to the point of its
      overlay. If no points fell within the cell, a zero value was
      assigned. Then a low pass filter was applied to the grid. The size of
      the filter and the coefficients used to weight the average towards
      the pixels were derived from the function that described the
      relationship between deforestation and distance from settlements
      pointed out in a previous step. Several layers were generated
      developing the model with different socioeconomic features. Values of
      the model layers were then grouped into ten classes representing
      similar area and rate of deforestation was calculated for each of
      them. Coefficient of correlation between the values of the model and
      the rates of deforestation were calculated. Analysis of the
      coefficients of correlation allows to determinate the relative
      influence of the different socioeconomic characteristics we
      integrated in the model on deforestation rates.

      I have some doubts about the model and the interpretation of the
      results: a) linear correlation is not always the more appropriate to
      estimate the relationship between the variables,is there a better way
      to estimate relation between two variables ? b) the studied
      socioeconomic variables presented correlation between them, there is a
      way to separate them ? c) the variable the model deals with is a
      combination of a sociological variable and the distance (because of
      the filtering), can be the variable "distance" be eliminated to
      analyze the pure socioeconomic variable ? Any suggestions or
      references about how to deal with socioeconomic point data and modeling
      influence of populated areas on its environment are welcome.

      *************************************************************
      THANKS YOU VERY MUCH TO:
      Vic Rudis, Alessandro Gimona, Vincent Simonneaux and G Nelson for
      their very interesting comments:



      *************************************************************
      A BRIEF SUMMARY OF THE RESPONSES:
      a) Logistic regression is a good way to estimate relationship
      between deforestation and exploratory variables. Stepwise log.
      regression allows to estimate relationship with each of the
      exploratory variables. it has been widely applied in species
      distribution studies.

      b) New decorrelated variables can be produced using an PCA but the
      problem is that new variables are linear combination of the
      socioeconomic variables. Therefore it can be less easy to interpret
      them.

      c) A way could be to subdivide a variable into several variable: e.g.
      subdivide the cities into several groups (large size, small size) and
      study separately the variable "distance from one attribute" (cities of a given size
      for example).

      *********************************************************
      I INCLUDE THE RESPONSES


      Date sent: Thu, 23 Jan 1997 12:19:56 -0600
      To: jeanfmas@...
      From: Gerald Nelson <g-nelson@...>
      Subject: socioecon factors

      Saw your note on the the geostats list and have a couple of
      references, two on Mexico and one on Belize, that might be of
      interest. I'd also like to stay in touch because what you are doing
      sounds like it is of great relevance for work I'm doing. Anyway, the
      references:


      Gerald Nelson and Daniel Hellerstein, "Do Roads Cause Deforestation?
      Using Satellite Images in Econometric Analysis of Land Use",
      forthcoming, American Journal of Agricultural Economics (with Daniel
      Hellerstein).


      Gerald Nelson and Daniel Hellerstein, "Deforestation in Central
      Mexico: Satellite Images as Data in Economic Models of Land Use ",
      forthcoming, Pecora 13 Conference Proceedings.


      You can get these from the web as pdf docs at
      http://w3.aces.uiuc.edu/ACE/faculty/GNelson/papers/paperindex.htm


      Chomitz, K. M., & Gray, D. A. (1995). Roads, Land, Markets and
      Deforestation: A Spatial Model of Land Use in Belize working paper,
      World Bank Policy Research Department, Environment Infrastructure, and
      Agriculture Division. Also published in World Bank Research Observer
      in summer 1996.


      A working paper by Klaus Deininger, also from the World Bank that
      incorporates population, wage rates, into a model that is similar to
      that used by the papers above.


      Let me know if you have any trouble getting these.


      By the way, I bounced into your web site. It looks interesting,
      although the English version wasn't working and my Spanish is a bit
      weak.


      Regards, Jerry Nelson



      Gerald C. Nelson

      Assoc. Professor, Department of Agricultural and Consumer Economics

      University of Illinois

      g-nelson@...

      217-333-6465

      Date sent: Tue, 21 Jan 1997 15:09:08 -0600 (CST)
      From: Vic Rudis <vrudis@...>
      To: jeanfmas@...
      Subject: Re: GEOSTATS: assessing socioeconomic factors
      influence in deforestation

      > Date: Tue, 21 Jan 1997 10:20:50 +1100
      > From: jeanfmas@...
      > To: ai-geostats@...
      > Subject: GEOSTATS: assessing socioeconomic factors influence in
      > deforestation
      >
      > I have some doubts about the model and the interpretation of the
      > results: a) linear correlation is not always the more appropriate to
      > estimate the relationship between the variables,is there a better
      > way to estimate relation between two variables ? b) the studied
      > socioeconomic variables presented correlation between them, there is
      > a way to separate them ? c) the variable the model deals with is a
      > combination of a sociological variable and the distance (because of
      > the filtering), can be the variable "distance" be eliminated to
      > analyze the pure socioeconomic variable ? Jean-Francois Mas
      > Laboratorio de Percepcion remota y SIG Programa EPOMEX Universidad
      > Autonoma de Campeche

      You did not say what type of correlations were done.
      1a. What you describe can be handled by logistic regression and
      point data. What the relationship shows is the "probability" of an
      association, rather than a linear relationship, between two variables.
      Equations predict point probabilities: 0o0c, 0o1c, 1o0c, and 1o1c,
      where 0=nonforested and 1=forested, and o=last time, and c=this time.

      1b. I used stepwise logistic regression and standardized coefficients,
      but there are other approaches. Standardized coefficients provide one
      with the "relative" importance of variables in the equations. See my
      paper in Landscape Ecology 10(5): 291-307, 1995, as one example of
      this approach. The article's model indicated those variables that best
      predicted forest fragment size class for south central U.S. bottomland
      hardwood forests. Proximity to agricultural land was the primary
      correlate, and that variable dominated the equations. Degree of nearby
      road development was a secondary predictor among small fragment
      classes and proximity to urban land was secondary among large fragment
      classes.

      1c. I don't know. One variable such as city population size could be
      subdivided into several variables: e.g., cities of large size, cities
      of small size. Then, distance from one attribute (cities of a given
      size class) to selected forest or nonforest points serve as potential
      variables for entry into a stepwise logistic regression equation.
      _ _
      /s/ Vic Rudis 1o,o1
      |Personal Page "http://www2.msstate.edu/~vrudis/index.html"
      | |U.S. forest ecoinventory
      "http://www.msstate.edu/Dept/Forestry/ecofia.html"| |U.S. forest
      survey (FIA) "http://www.srsfia.usfs.msstate.edu/" |
      |Other regional surveys
      "http://www2.msstate.edu/~vrudis/fiaaffinity.html"|
      \------------ Opinions expressed are mine and not my
      employer's.----------/
      >

      From: geo293@...
      Subject: Re: GEOSTATS: assessing socioeconomic factors
      influence in deforestation To: jeanfmas@...
      Date sent: Tue, 21 Jan 1997 19:11:57 +0000 (GMT)

      >
      > We are studying deforestation in the State of Campeche (South east
      > of
      > Mexico). Overlaying two forest maps of different dates, we
      > generated
      > a digital map which represents the evolution of forest areas.
      > Classes of this digital map are non forested areas, unchanged
      > forest areas and deforested areas. As a following step, we tried to
      > find out the relationship between deforestation and environmental
      > and socioeconomic factors such as distance from settlements and
      > characteristics of the population.
      >
      > Socioeconomic data we used are organized by localities (data are
      > given for each settlement), the problem is how to deal with both
      > point data and surface data within a GIS. In order to represent the
      > influence of a settlement on its environment we developed a simple
      > model which is based on two simplifying assumptions: the influence
      > of a settlement a) depends on its characteristics and b) decreases
      > with the distance from it.
      >
      > In order to develop the model, localization of each settlements was
      > captured in a point cover, then the settlements point cover was
      > converted to a grid using. The value assigned to the grid cells was
      > the value of a selected property (for example number of people of
      > the settlement) according to the point of its overlay. If no points
      > fell within the cell, a zero value was assigned. Then a low pass
      > filter was applied to the grid. The size of the filter and the
      > coefficients used to weight the average towards the pixels were
      > derived from the function that described the relationship between
      > deforestation and distance from settlements pointed out in a
      > previous step. Several layers were generated developing the model
      > with different socioeconomic features. Values of the model layers
      > were then grouped into ten classes representing similar area and
      > rate of deforestation was calculated for each of them. Coefficient
      > of correlation between the values of the model and the rates of
      > deforestation were calculated. Analysis of the coefficients of
      > correlation allows to determinate the relative influence of the
      > different socioeconomic characteristics we integrated in the model
      > on deforestation rates.
      >
      > I have some doubts about the model and the interpretation of the
      > results: a) linear correlation is not always the more appropriate to
      > estimate the relationship between the variables,is there a better
      > way to estimate relation between two variables ?

      (I am not entirely clear as to all the steps especially
      the filtering one, so apologies if this does not
      make sense).

      you could try ordinal logistic regression

      b) the studied
      > socioeconomic variables presented correlation between them, there is
      > a way to separate them ?



      You could use a stepwise technique and/or run
      Principal component analysis on the data array and use the PC scores
      as new input in step a)

      c) the variable the model deals with is a

      > combination of a sociological variable and the distance (because of
      > the filtering), can be the variable "distance" be eliminated to
      > analyze the pure socioeconomic variable ? Any suggestions or
      > references about how to deal with socioeconomic point data and
      > modeling
      > influence of populated areas on its environment are welcome.
      >


      Sorry, I don't know about this point.


      A last note: if the data present spatial autocorrelation in the
      dependent variable you might have to use a spatial simulation approach
      to test for significance.

      'HOPE IT HELPS

      Alessandro gimona
      MLURI
      Aberdeen
      Scotland

      Date sent: Fri, 24 Jan 1997 18:39:19 +0100
      From: simonne@... (Vincent
      Simonneaux) To: jeanfmas@... Subject:
      Re: GEOSTATS: assessing socioeconomic factors influence in
      deforestation


      Bonjour,

      Je suppose que tu comprends le francais.
      Le resume de cette question m'interessera car nous aurons bientot a
      etudier egalement des Pb semblables Pour ce qui est de tes questions,
      quelques reflexions

      a) il existe effectivement d'autres methodes d'etude des correlations
      qui sont non parametriques (ex: coeff de coor. de rang de spearman,
      "statistique S", Khi2 entre deux distributions). (Le coeff de corr
      classique fait l'hypo gaussienne) C'est explique dans tout manuel de
      stats basique

      b) Tu ne peux supprimer les correlations entre variables qu'en en
      creant d'autres non correlees (ex: Analyse en composantes principales
      pour les variables quantitatives, mais difficile pour les
      qualitatives)

      c) Je ne comprends pas bien ou est le Pb. La variable socio "pure"
      n'existe pas il faut bien que tu la localises dans l'espace (?). (Et
      dans ce cas, si la pression socio est liee a la distance et la
      deforestation a la pression, alors tu ne pourras pas empecher la
      deforestation d'etre liee a la distance.

      ----------------------------------------------------------------------
      ------- Vincent Simonneaux ORSTOM (Institut FranTauais de Recherche
      Scient. pour le Developpement en Cooperation) 32 Avenue Henri Varagnat
      93143 BONDY Cedex Tel: (1) 48 02 55 77 / Fax: (1) 48 47 30 88 /
      Email: simonneaux@...
      ______________________________________________________________________
      _______

      *************************************************
      Jean-Francois Mas
      Laboratorio de Percepcion Remota y SIG
      Centro EPOMEX
      Universidad Autonoma de Campeche
      AP 520 CP 24030 CAMPECHE, CAMP, MEXICO

      Tel (52) (981) 11600
      Fax (52) (981) 65954

      E-mail jeanfmas@...
      WWW : epomex.uacam.mx
      *************************************************
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    • Harini Nagendra
      a] ... for starters.. you can rank the 2, i.e. your model and actual data, and check if the 2 ranks correspond... positively or negatively.. if there is a rank
      Message 2 of 2 , Feb 15, 1997
        a] ... for starters.. you can rank the 2, i.e. your model and actual
        data, and check if the 2 ranks correspond... positively or negatively..
        if there is a rank correlation then that suggets that there is some other
        kind of correlation.. after which you can try linear, quadratic etc.
        Another good thing wouold be to visually look at tye graphs.. that will
        give you some idea about whether there is some sort of relationship
        between the wto, or your points are merely randomly distributed.
        b].....do a principal components analysis on your socioeconomic
        variables.. and repeat the correlation studies on the 1st, 2nd etc
        components. If you get significant results, then look at each component
        and see what it is composed of.. i.e. how much each socioeconomic
        variable contributes to this component.. of course, this approach has
        problems which have already been pointed out..
        c].....if you want to eliminate distance.. I guess one thing you can do is,
        treat the variable as having equal influence over some fixed distance..
        and after that no influence.. what this distance is is something you will
        have to decide on based on your "feel" for the data..
        Hope this helps..
        Harini Nagendra
        Centre for Ecological Sciences
        Indian Institute of Science
        >
        > I have some doubts about the model and the interpretation of the
        > results: a) linear correlation is not always the more appropriate to
        > estimate the relationship between the variables,is there a better way
        > to estimate relation between two variables ? b) the studied
        > socioeconomic variables presented correlation between them, there is a
        > way to separate them ? c) the variable the model deals with is a
        > combination of a sociological variable and the distance (because of
        > the filtering), can be the variable "distance" be eliminated to
        > analyze the pure socioeconomic variable ? Any suggestions or
        > references about how to deal with socioeconomic point data and modeling
        > influence of populated areas on its environment are welcome.
        >
        --
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