Land Use Planning
Maximiliano Gobbo
2024-04-24
Land Use Planning
Introduction
Throughout this research, we carried out analysis, mining, manipulation, plotting and mapping (static and interactive) of census data (IBGE, 2013a) and land use and cover (IBGE, 2013b). These data were used with the aim of mapping anthromes in the State of São Paulo, a region defined as a pilot area due to its economic, political, social, cultural and environmental representativeness, as demonstrated in the previous chapters and as the authors pointed out.
The disjunction of the mapping of populated anthromes and land use and cover anthromes allowed a visualization of how population groupings and land uses are associated with the classes of anthropogenic biomes, first defined by Ellis and Ramankutty (2008) and reiterated in others work in this area of knowledge. Furthermore, the analyzes carried out in each of them made it possible to study uncertainty and validate our mappings, where we verified that the products generated separately have a significant capacity for spatialization of geographic information, clarity in information and representation, as well as a level relevant indicators of selectivity, specificity and error and overall accuracy.
With these promising results from products generated in programming language (R software), we began to combine the mapping of populated anthromes with land use and land cover anthromes. The junction, also carried out in R, was handled in two stages. We first focused on static mapping, in order to generate a cartographic product that could be printed and/or inserted into other materials, without losing the definition of the pixels that made it up. Secondly, we focus on bringing together interactive mappings, in order to structure a mapping for integration into different web, desktop and/or mobile platforms.
From this perspective, this chapter is divided into three main moments: 1. Static Mapping of São Paulo anthromes; 2. Interactive Mapping of São Paulo anthromes, and; 3. Analysis of Static Mapping as a Territorial Planning tool. This last item, as foreseen in the Thesis Methodology, dealt with the study of mapping and its applicability in practices associated with planning and territorial ordering. To this end, it brings a set of reflections on the product and its articulations with national social, cultural, political and environmental aspects and the socio-ecological analytical guidelines of the concept of anthromes.
It is worth mentioning that, considering the regionalization of the mapping and the concept, we brought an expanded approach to anthromes, integrating sociocultural aspects of the different population clusters and the different typologies of land use and coverage in the São Paulo territory. Likewise, we seek to insert and support our reflections in texts, works, policies, standardizations, regulations and national legal system, considering that this is a work for the Brazilian nation and aligns with efforts to achieve sustainable development and to compliance with the precept described by article 225 of the Magna Carta of Brazil (Brazil, 1988).
Uploading demographic data
getwd()## [1] "C:/ARQUIVOS COMPUTADOR/DOUTORADO/DOUTORADO TESE/03 DADOS GEOESPACIAIS/04.1 LAND PLAN"The first moment reflected our return to the data sets used in previous chapters. To do so, we reload the base datasets and perform the data mining, filtering and correlation processes. As we explained in previous chapters and demonstrated why we used the following sets, here we only brought data from population01 and localsp to recreate the set populated_anthromes (populated anthromes) and ** coversp** for anthromes_ucs_sp (land use and land cover anthromes).
The procedures used to recreate the sets were the same as those used in the previous two chapters. Therefore, we do not present the codes that gave rise to the two sets of anthromes (populated_anthromes and anthromes_ucs_sp). In order to refresh the reader’s memory, we report below only the plots of data from both sets, creating the visual atmosphere for joining the sets. Thus, Figure 1 represents the plot of data from populated_anthromes and Figure 2 refers to data from anthromes_ucs_sp.
Figure: populated anthromes
Source: the authors (2024). Caption: plot of the data set referring to populated anthromes (populated_anthromes). The figure shows the different types of population clusters described in the territory of São Paulo.
Figure: Land Use and Cover Anthromes.
Source: the authors (2024). Legend: plot of the data set referring to anthromes of land use and cover (anthromes_ucs_sp). The figure shows the different typologies of land use and cover described in the territory of São Paulo.
Joining Static Mappings
Having reported the sets above, we move on to joining the static mappings of the anthromes. The sets used in this procedure are indicated in the following sequence:
Usage and Coverage:
legend_anthromes_ucs
anthromes_ucs_sp
Demographics:
populated_anthromes
legend_populatedanthromes
Join:
colors_categories
order_categories
It is noteworthy that the data used for Junction refers to the characteristics of the captions, because, as we previously demonstrated and reported in previous chapters, each of the sets (anthromes_ucs_sp and populated_anthromes) already had specific captions. To this end, here we synthesize the two subtitles into a single one, creating what we presented above as junction.
The procedure for creating colors_categories and order_categories followed the structure for creating data frames (data sets). For the first (colors_categories) we structured two columns, one with the categories (in English, category) of anthromes present in the State of São Paulo; the other column with the colors (colors) referring to each of the anthrome typologies. This structuring led to the creation of a set of 16 lines and 2 columns, where the lines represented the different anthromes identified in the Federation Unit and their respective color for mapping.
On the other hand, in order_categories we carry out the sequential organization for the legend, that is, the logical order of the categories and their respective colors. In this case, there was no creation of a data frame, just a sequential set of information to order the creation of the legend and the use of colors in the mapping.
## Warning in geom_point(data = populated_anthromes, aes(x = longitude, y =
## latitude, : Ignoring unknown parameters: `width` and `height`
Script: Construction of static mapping of São Paulo anthromes.
# Static mapping of anthromes
anthromes_static <- ggplot(anthromes_ucs, aes(fill = factor(color, levels = unique(legend_anthromes_ucs$colors)))) +
geom_sf(color = NA) +
scale_fill_manual(values = legend_anthromes_ucs$colors,
labels = legend_anthromes_ucs$category,
name = "category")+
geom_point(data = populated_anthromes, aes(x = longitude, y = latitude, color = category), pch= 16, size = 1, inherit.aes = FALSE)+
scale_color_manual(values = setNames(colors_categories$colors, colors_categories$category), breaks = order_categories, labels = order_categories)+
labs (title = "Anthromes", subtitle = "Study Area: State of São Paulo (Brazil)", fill = colors_categories$category)+
theme_minimal()Source: the authors (2024). Caption: script presenting the code structure for creating the static mapping of São Paulo anthromes using the ggplot() function.
Assuming the prediction, we move on to structuring the static mapping of São Paulo anthromes. For creation, we used the ggplot() function and its accessory functions to organize the data in the mapping. As the main mapping layer, we used the data from anthromes_ucs, determining the coloring for plotting through the operator fill = factor(cor, levels = unique(legend_anthromes_ucs$cor)), where the levels for coloring were used from the base legend_anthromes_ucs. Additionally, we removed the edges of the polygons from anthromes_ucs_sp through geom_sf (color = NA), since, as previously presented, this would make it impossible to visualize the polygons’ colors. Sequentially, we determine the creation of the data legend through scale_fill_manual(), determining the values of the colors, categories and their respective names.
Later, we added the layer with the points associated with populated anthromes (populated_anthromes), using the geom_point() function. In the same way as in the chapter on populated anthromes, we use the format of points (pch = 16), the size of 1 for the points (size = 1) and the latitude and longitude information (X and Y) to determine the spatialization of the data in the mapping. The objective of the argument inherit.aes = FALSE was to determine the ggplot() function to superimpose the layer of points of the populated anthromes on the mapping generated for the land use and cover anthromes.
Additionally, in scale_color_manual() we determine the creation of the legend for the populated anthrome points. Separate creation was necessary because, as they were different data types (anthromes_ucs_sp = shape file/raster; populated_anthromes = vector/points), the ggplot() function did not make it possible to create a single subtitle. Next, Figure 3 represents the Interactive Mapping of anthromes in São Paulo.
Figure: Static Mapping of Anthromes of São Paulo (Brazil)
Source: the authors (2024). Caption: static mapping of anthromes classified in the territory of São Paulo. Land use and cover data form the basis of the mapping, while demographic data are scored on this basis, forming a mosaic that allows the identification of the different anthrome typologies described for São Paulo.
Joining Interactive Mappings
Once the static mapping of São Paulo anthromes was completed, we dedicated ourselves to structuring their interactive mapping. The objective at this stage was to merge the two mappings created in previous chapters, establishing the relationship between spatialities and their demographic characteristics. In other words, we tried, through interactive mapping, to streamline the visualization of demographic occupation in the different contexts of land use and cover in the anthromes of São Paulo.
In this sense, we first reproduced in this script the codes associated with each of the interactive mappings created previously (populatedanthromes_map and anthromes_ucs_interativo), in order to verify that the coded structures still generated the previous products. As they are the same products presented in the previous chapters, we do not reproduce the codes here; We only run and check products (codes hidden).
Considering this, we started creating the interactive mapping of São Paulo anthromes using the leaflet() function. As we have previously described, this function uses the Open Street Maps (OSM) mapping base, a free and collaborative collection of maps covering different global contexts. We determined, at the beginning of the work, that the mapping would be generated in the region of the State of São Paulo, using the argument setView (lng = -47.9292, lat = -23.5505, zoom = 7), where the georeferenced characteristics are established latitude and longitude for zooming in the mapping, as illustrated in the Script.
Just like in static mapping, in this step we use land use and land cover data as a basis to create the base layer on the OSM structure. In other words, the first data entered into the interactive mapping were those relating to land use and land cover anthromes. We chose this path because the land use and cover data set is structurally represented by polygons, generating more complete coverage over the OSM base. Furthermore, as these data are responsible for the entire coverage of the State, they are able to describe the different typologies and cover them sufficiently.
The layers referring to land use and land cover anthromes were inserted one by one, in order to reduce processing time, given the volume of data to which they referred. Therefore, in the Script it is possible to visualize the addition of layers in the interactive_anthromes set through the interactive_anthromes <- interactive_anthromes %>% addPolygons() operators. In this model, the %>% function is responsible for inserting the new information/data layer; In this case, we used addPolygons() for the land use and cover layers (area polygons), addRectangles() for the points that represented population clusters and addLegend() to add the legend referring to the mapping interactive.
It should be noted that the colorimetric standard used to create the interactive mapping corresponds to the same one previously used in the creation of isolated interactive mappings. The data set used to generate the legend and establish the colorimetric standard was colors_categories, the one we previously structured for static mapping.
Script: Code for creating interactive mapping of São Paulo anthromes.
# Create a Leaflet map based on OpenStreetMap and add the artificial area layer
interactive_anthromes <- leaflet() %>%
addTiles() %>%
setView(lng = -47.9292, lat = -23.5505, zoom = 7) %>%
addPolygons(data = artificial_area_multipolygons,
color = NA,
fillColor = "#FF4747",
fillOpacity = 1,
popup = "Artificial Area")
# Add agricultural Lands layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = agricultural_lands_multipolygons,
color = NA,
fillColor = "#F4B488",
fillOpacity = 1,
popup = "Agricultural Lands")
# Add Livestock Lands layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = livestock_lands_multipolygons,
color = NA,
fillColor = "#FFEEB7",
fillOpacity = 1,
popup = "Livestock Lands")
# Add Sustainable Use layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = sustainable_use_multipolygons,
color = NA,
fillColor = "#A8D08D",
fillOpacity = 1,
popup = "Sustainable Use")
# Add Silvicuture layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = silvicuture_multipolygons,
color = NA,
fillColor = "#E2EFD9",
fillOpacity = 1,
popup = "Silviculture")
# Add Conservation and Preservation layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = conservation_preservation_multipolygons,
color = NA,
fillColor = "#538135",
fillOpacity = 1,
popup = "Conservation and Preservation")
# Add Agricultural Villages layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = agricultural_villages_multipolygons,
color = NA,
fillColor = "#ED833B",
fillOpacity = 1,
popup = "Agricultural Villages")
# Add Waters layer
interactive_anthromes <- interactive_anthromes %>% addPolygons(data = waters_multipolygons,
color = NA,
fillColor = "#000066",
fillOpacity = 1,
popup = "Waters")
# Add colored squares corresponding to the categories on the second map
interactive_anthromes <- interactive_anthromes %>%
addRectangles(data = populated_anthromes,
lng1 = ~longitude - 0.005,
lat1 = ~latitude - 0.005,
lng2 = ~longitude + 0.005,
lat2 = ~latitude + 0.005,
color = ~legend_populatedanthromes$colors[match(category, legend_populatedanthromes$category)],
fillOpacity = 1,
popup = ~paste("category", category)) %>% clearMarkers()
# Add the combined legend to the map
interactive_anthromes <- interactive_anthromes %>%
addLegend(position = "bottomright",
colors = colors_categories$colors,
labels = colors_categories$category,
title = "Anthromes")Source: the authors (2024). Caption: Coded structure for creating interactive mapping of São Paulo anthromes. According to what was reproduced, each of the layers were inserted individually, in order to reduce the data processing time for mapping.
Using this code, we were able to structure the interactive mapping of São Paulo anthromes on the OSM database. The mapping reveals the different types of land use and coverage of anthromes and the areas where population clusters are found, as shown in the following figure.
Figure: Interactive Mapping of São Paulo’s Anthromes.