GEOG 172: INTERMEDIATE GEOGRAPHICAL ANALYSIS
FALL 2022
Evgeny Noi
About me¶
 |
PhD candidate at UCSB Dept of Geography. I specialize in spatio-temporal visualizations that help exploratory spatial data analysis and data-driven knowledge discovery. In my day-to-day tasks I prefer using FOSS (free and open source) tools (e.g. Python, QGIS, GeoDa, R, PostGIS) that help make research more reproducible. |
The outline of today's lecture¶
- Course Introduction
- Syllabus
- Weekly Patterns
- Success tips
- GIS versus Geographical Analysis
Course logistics¶
- GauchoSpace - dropboxes for quizzes and labs + announcements
- JupyterBook course page - Syllabus, course materials, lab instructions
Evaluation¶
- Labs (40%) - the total of 8 labs. Lab 9 and 10 (as per Syllabus schedule) will be devoted to projects mostly.
- Project (30%) - 4 deliverables.
- Data Report (5%)
- Interim Progress Report (5%)
- Presentation (10%)
- Final Report (10%)
- Weekly quizzes (20%) - multiple choice and short answers. Lowest quiz will be dropped.
- Participation (10%) - labs + lectures.
Labs¶
- In-person (M/T). Switching Policy.
- Python, GeoDa, ArcGIS / QGIS
- Due the following week before section!
- Late Policy: 1% for each hour late
- See schedule in the Syllabus
Why Project?¶
- Projects mimic the on-the-job duties better.
- Spatial data tasks:
- Acquire data (not part of this course)
- Process data (partly covered)
- Explore the data (covered)
- Generate hypotheses about the data
- Test hypothesis with statistics
Project structure¶
- Data report. Find spatial data and describe in Python using code snippets learned in lab sections.
- Interim data report. Utilize spatial statistics to characterize your data spatially.
- Project presentation. Week 10. Sign-up for time either during lecture time or during lab time.
- Final Report.
Quizzes¶
- Short answers and mulitple choice. Every Thursday: 8.00 - 23.00. Lowest score - dropped.
Participation¶
- Required for both labs and lectures
| Wk | Date | Topics | Readings |
|---|---|---|---|
| 1 | 09/26 | Introduction/Review of Spatial Analysis | GIA Ch1 |
| 1 | 09/28 | Review of Basic Statistics | TBD |
| 2 | 10/3 | Geographic Data | SD |
| 2 | 10/5 | Distance, adjacency and MAUP | GIA Ch1 |
| 3 | 10/10 | Geovisualization | GIA Ch3 |
| 3 | 10/12 | Point Pattern Analysis | GIA Ch5,6 |
| 3 | 10/16 | Data Report Due | ⚠️ |
| 4 | 10/17 | Correlation and Covariance | |
| 4 | 10/19 | ANOVA | |
| 5 | 10/24 | Spatial Autocorrelation 1 | GIA Ch7 |
| 5 | 10/26 | Spatial Autocorrelation 2 |
| Wk | Date | Topics | Readings |
|---|---|---|---|
| 6 | 10/31 | LISA and Local Moran's $I$ | GIA Ch8 |
| 6 | 11/2 | Other Local Statistics | |
| 6 | 11/6 | Interim Data Report Due | ⚠️ |
| 7 | 11/7 | Spatial Regression 1 | SR |
| 7 | 11/9 | Spatial Regression 2 | |
| 8 | 11/14 | Spatial Clustering/Regionalization 1 | CR |
| 8 | 11/16 | Spatial Clustering/Regionalization 2 | |
| 9 | 11/21 | Spatial Interpolation | GIA Ch9 |
| 9 | 11/23 | Geostatistics | GIA Ch10 |
| 9 | 11/27 | Presentation slides due | ⚠️ |
| 10 | 11/28 | Presentations 1 | |
| 10 | 11/30 | Presentations 2 | |
| 11 | 12/7 | Final Report Due | ⚠️ |
Weekly Class Schedule¶
- M/W: lectures (see Syllabus for readings)
- M/T: lab sections (see Syllabus for lab schedule). Submit labs before the section the following week.
- R: take a quiz
Questions 🍺?¶
Geographic Information Analysis¶
- Geographic information analysis
- Concerned with investigating patterns that arise as processes that may be operating in space
- Techniques and methods that enable the representation, description, measurement, comparison, and generation of spatial patterns
- What is a pattern?
- Potential realization of a process
- Provide clues to the cause of processes
- What is a process?
- The underlying phenomenon that operates in space
Geographic Information Analysis¶
- Geographic Information Systems
- Remote Sensing
- Exploratory Spatial Data Analysis (ESDA)
- Statistics / Geostatistics
- Simulation / Agent-based Modeling / Cellular Automata
- Spatial Decision Support Systems
- Geovisualization
GIS¶
- System of hardware, software & procedures designed to support geographical decision making through the capture, management, manipulation, analysis, modeling and display of spatially referenced data
- Can handle geographic (spatially referenced) data or non-spatial attribute data
- Operations include:
- Summary statistics, distance, intersection, buffer, map overlay, projection, transformation, etc.
Here is what GIS is (in Python words)¶
In [1]:
import geopandas as gpd
import osmnx as ox
import matplotlib.pyplot as plt
import contextily as cx
from IPython.display import Image
from pointpats import centrography
from matplotlib.patches import Ellipse
import numpy as np
ox.settings.use_cache = True
In [2]:
goleta = ox.geocode_to_gdf("Goleta, CA, USA") # get Goleta
go_graph = ox.graph_from_place('Goleta, CA, USA')
go_nodes, go_streets = ox.graph_to_gdfs(go_graph)
tags = {'amenity': ['pub', 'bar', 'cafe', 'restaurant']} # used for parsing OSM data
dist = 5000 # set search radius (in meters)
# download POIs
pois = ox.geometries.geometries_from_point(center_point = (goleta.lat[0],goleta.lon[0]), tags=tags, dist=dist)
go_proj = ox.project_gdf(goleta, to_crs='EPSG:3857') # re-project layers
go_streets_proj = ox.project_gdf(go_streets, to_crs='EPSG:3857')
pois_proj = ox.project_gdf(pois, to_crs='EPSG:3857')
# print to the output
print(f'There are {pois.shape} cafes/restaurants/bars/pubs in SB!')
pois.head(3)
There are (85, 58) cafes/restaurants/bars/pubs in SB!
Out[2]:
| amenity | brand | brand:wikidata | brand:wikipedia | cuisine | name | official_name | takeaway | geometry | source | ... | disused:building | capacity | height | roof:colour | roof:shape | contact:facebook | addr:housename | designation | ways | type | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| element_type | osmid | |||||||||||||||||||||
| node | 448863565 | cafe | Starbucks | Q37158 | en:Starbucks | coffee_shop | Starbucks | Starbucks Coffee | yes | POINT (-119.84817 34.41147) | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1341709739 | restaurant | NaN | NaN | NaN | american | IHOP | NaN | NaN | POINT (-119.78818 34.44325) | Local Knowledge | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | |
| 1348747781 | restaurant | NaN | NaN | NaN | indian | Masala Spice Indian Cuisine | NaN | NaN | POINT (-119.82570 34.44172) | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
3 rows × 58 columns
In [3]:
go_streets_proj.highway.value_counts()[:15]
Out[3]:
service 15890 footway 12784 residential 7977 path 1446 tertiary 772 secondary 644 unclassified 249 cycleway 150 track 84 [footway, service] 60 [footway, residential] 60 motorway_link 44 [footway, path] 30 motorway 27 living_street 14 Name: highway, dtype: int64
In [20]:
fig, ax = plt.subplots(figsize=(15,15))
major_str = go_streets_proj.loc[(go_streets_proj.highway=='secondary')|(go_streets_proj.highway=='primary')|(go_streets_proj.highway=='motorway')]
go_proj.plot(fc="blue", ec="blue", linewidth=3, alpha=.25, ax=ax, zorder=1, label='Goleta')
major_str.plot(color='k', ax=ax, zorder=2)
go_proj.centroid.plot(color='orange', markersize=200, marker='*', ax=ax, zorder=3)
go_proj.centroid.buffer(5000).plot(fc='none', ec='orange', linewidth=2, ax=ax, linestyle='dashed', zorder=4)
pois_proj.plot(column='amenity', markersize=20, ax=ax, categorical=True, legend=True, zorder=5)
cx.add_basemap(ax=ax)
ax.set_title('Cafes, restaraunts and bars\n within 5000m of Goleta,CA')
_ = ax.axis("off")
fig.savefig('goleta_cafe.png', bbox_inches='tight')
plt.close()
In [21]:
Image(filename='goleta_cafe.png')
Out[21]:
In [6]:
# create x and y for calculation with projected coords (meters)
pois = pois.to_crs('EPSG:3857')
pois['x'] = pois.geometry.centroid.x
pois['y'] = pois.geometry.centroid.y
In [7]:
mean_center = centrography.mean_center(pois[["x", "y"]])
med_center = centrography.euclidean_median(pois[["x", "y"]])
std_dist = centrography.std_distance(pois[["x", "y"]])
major, minor, rotation = centrography.ellipse(pois[["x", "y"]])
print(mean_center)
print(med_center)
print(std_dist)
[-13341454.75696344 4086141.85634311] [-13342101.87019569 4085474.32197616] 2964.6474778578227
C:\Users\barguzin\anaconda3\envs\geo_env\lib\site-packages\pointpats\geometry.py:22: DeprecationWarning: Please use `ConvexHull` from the `scipy.spatial` namespace, the `scipy.spatial.qhull` namespace is deprecated. spatial.qhull.ConvexHull, C:\Users\barguzin\anaconda3\envs\geo_env\lib\site-packages\pointpats\geometry.py:42: DeprecationWarning: Please use `ConvexHull` from the `scipy.spatial` namespace, the `scipy.spatial.qhull` namespace is deprecated. def _(shape: spatial.qhull.ConvexHull): C:\Users\barguzin\anaconda3\envs\geo_env\lib\site-packages\pointpats\geometry.py:85: DeprecationWarning: Please use `ConvexHull` from the `scipy.spatial` namespace, the `scipy.spatial.qhull` namespace is deprecated. def _(shape: spatial.qhull.ConvexHull): C:\Users\barguzin\anaconda3\envs\geo_env\lib\site-packages\pointpats\geometry.py:132: DeprecationWarning: Please use `ConvexHull` from the `scipy.spatial` namespace, the `scipy.spatial.qhull` namespace is deprecated. def _(shape: spatial.qhull.ConvexHull, x: float, y: float): C:\Users\barguzin\anaconda3\envs\geo_env\lib\site-packages\pointpats\geometry.py:174: DeprecationWarning: Please use `ConvexHull` from the `scipy.spatial` namespace, the `scipy.spatial.qhull` namespace is deprecated. def _(shape: spatial.qhull.ConvexHull):
In [17]:
# Set up figure and axis
fig, ax = plt.subplots(1, figsize=(9, 9))
# Plot photograph points
ax.scatter(pois["x"], pois["y"], s=2)
ax.scatter(*mean_center, color="red", marker="x", label="Mean Center")
ax.scatter(
*med_center, color="limegreen", marker="o", label="Median Center"
)
# Construct the standard ellipse using matplotlib
ellipse = Ellipse(
xy=mean_center, # center the ellipse on our mean center
width=major * 2, # centrography.ellipse only gives half the axis
height=minor * 2,
angle=np.rad2deg(
rotation
), # Angles for this are in degrees, not radians
facecolor="none",
edgecolor="red",
linestyle="--",
label="Std. Ellipse",
)
ax.add_patch(ellipse)
ax.legend()
# Display
# Add basemap
cx.add_basemap(
ax, source=cx.providers.Stamen.TonerLite
#ax, source=cx.providers.OpenStreetMap.Mapnik
)
fig.savefig('centro.png', bbox_inches='tight')
plt.close()
In [18]:
Image(filename='centro.png')
Out[18]:
