Hugging Face's logo

Spaces:
yunusserhat
/
Crime-Map
Sleeping

App Files Community
Crime-Map
File size: 6,579 Bytes 
bb4a729
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
 
import ee
import streamlit as st
import geemap.foliumap as geemap

WIDTH = 1060
HEIGHT = 600


def function():
    st.write("Not implemented yet.")
    Map = geemap.Map()
    Map.to_streamlit(WIDTH, HEIGHT)


def lulc_mrb_floodplain():

    Map = geemap.Map()

    State_boundaries = ee.FeatureCollection('users/giswqs/MRB/State_Boundaries')
    State_style = State_boundaries.style(
        **{'color': '808080', 'width': 1, 'fillColor': '00000000'}
    )

    MRB_boundary = ee.FeatureCollection('users/giswqs/MRB/MRB_Boundary')
    MRB_style = MRB_boundary.style(
        **{'color': '000000', 'width': 2, 'fillColor': '00000000'}
    )

    floodplain = ee.Image('users/giswqs/MRB/USGS_Floodplain')

    class_values = [34, 38, 46, 50, 62]
    class_palette = ['c500ff', '00ffc5', '00a9e6', '73004d', '004d73']

    img_1950 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1950')
    img_1950 = img_1950.set('b1_class_values', class_values)
    img_1950 = img_1950.set('b1_class_palette', class_palette)

    img_1960 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1960')
    img_1960 = img_1960.set('b1_class_values', class_values)
    img_1960 = img_1960.set('b1_class_palette', class_palette)

    img_1970 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1970')
    img_1970 = img_1970.set('b1_class_values', class_values)
    img_1970 = img_1970.set('b1_class_palette', class_palette)

    img_1980 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1980')
    img_1980 = img_1980.set('b1_class_values', class_values)
    img_1980 = img_1980.set('b1_class_palette', class_palette)

    img_1990 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_1990')
    img_1990 = img_1990.set('b1_class_values', class_values)
    img_1990 = img_1990.set('b1_class_palette', class_palette)

    img_2000 = ee.Image('users/giswqs/MRB/Major_Transitions_1941_2000')
    img_2000 = img_2000.set('b1_class_values', class_values)
    img_2000 = img_2000.set('b1_class_palette', class_palette)

    Map.addLayer(floodplain, {'palette': ['cccccc']}, 'Floodplain', True, 0.5)
    Map.addLayer(img_2000, {}, 'Major Transitions 1941-2000')
    Map.addLayer(img_1990, {}, 'Major Transitions 1941-1990')
    Map.addLayer(img_1980, {}, 'Major Transitions 1941-1980')
    Map.addLayer(img_1970, {}, 'Major Transitions 1941-1970')
    Map.addLayer(img_1960, {}, 'Major Transitions 1941-1960')
    Map.addLayer(img_1950, {}, 'Major Transitions 1941-1950')

    Map.addLayer(State_style, {}, 'State Boundaries')
    Map.addLayer(MRB_style, {}, 'MRB Boundary')

    Map.to_streamlit(WIDTH, HEIGHT)


def global_mangrove_watch():
    """https://samapriya.github.io/awesome-gee-community-datasets/projects/mangrove/"""
    Map = geemap.Map()
    gmw2007 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2007_v2")
    gmw2008 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2008_v2")
    gmw2009 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2009_v2")
    gmw2010 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2010_v2")
    gmw2015 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2015_v2")
    gmw2016 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_2016_v2")
    gmw1996 = ee.FeatureCollection("projects/sat-io/open-datasets/GMW/GMW_1996_v2")

    Map.addLayer(
        ee.Image().paint(gmw1996, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 1996',
    )
    Map.addLayer(
        ee.Image().paint(gmw2007, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2007',
    )
    Map.addLayer(
        ee.Image().paint(gmw2008, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2008',
    )
    Map.addLayer(
        ee.Image().paint(gmw2009, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2009',
    )
    Map.addLayer(
        ee.Image().paint(gmw2010, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2010',
    )
    Map.addLayer(
        ee.Image().paint(gmw2015, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2015',
    )
    Map.addLayer(
        ee.Image().paint(gmw2016, 0, 3),
        {"palette": ["228B22"]},
        'Global Mangrove Watch 2015',
    )

    Map.to_streamlit(WIDTH, HEIGHT)


def app():

    st.title("Awesome GEE Community Datasets")

    st.markdown(
        """
    
    This app is for exploring the [Awesome GEE Community Datasets](https://samapriya.github.io/awesome-gee-community-datasets). Work in progress.
    
    """
    )

    datasets = {
        "Population & Socioeconomic": {
            "High Resolution Settlement Layer": "function()",
            "World Settlement Footprint (2015)": "function()",
            "Gridded Population of the World": "function()",
            "geoBoundaries Global Database": "function()",
            "West Africa Coastal Vulnerability Mapping": "function()",
            "Relative Wealth Index (RWI)": "function()",
            "Social Connectedness Index (SCI)": "function()",
            "Native Land (Indigenous Land Maps)": "function()",
        },
        "Geophysical, Biological & Biogeochemical": {
            "Geomorpho90m Geomorphometric Layers": "function()",
        },
        "Land Use and Land Cover": {
            "Global Mangrove Watch": "global_mangrove_watch()",
            "Mississippi River Basin Floodplain Land Use Change (1941-2000)": "lulc_mrb_floodplain()",
        },
        "Hydrology": {
            "Global Shoreline Dataset": "function()",
        },
        "Agriculture, Vegetation and Forestry": {
            "Landfire Mosaics LF v2.0.0": "function()",
        },
        "Global Utilities, Assets and Amenities Layers": {
            "Global Power": "function()",
        },
        "EarthEnv Biodiversity ecosystems & climate Layers": {
            "Global Consensus Landcover": "function()",
        },
        "Weather and Climate Layers": {
            "Global Reference Evapotranspiration Layers": "function()",
        },
        "Global Events Layers": {
            "Global Fire Atlas (2003-2016)": "function()",
        },
    }

    row1_col1, row1_col2, _ = st.columns([1.2, 1.8, 1])

    with row1_col1:
        category = st.selectbox("Select a category", datasets.keys(), index=2)
    with row1_col2:
        dataset = st.selectbox("Select a dataset", datasets[category].keys())

    Map = geemap.Map()

    if dataset:
        eval(datasets[category][dataset])

    else:
        Map = geemap.Map()
        Map.to_streamlit(WIDTH, HEIGHT)